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Keywords = biomass-to-energy

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24 pages, 4980 KiB  
Article
Optimization of a Biomass-Based Power and Fresh Water-Generation System by Machine Learning Using Thermoeconomic Assessment
by Fatemeh Parnian Gharamaleki, Shayan Sharafi Laleh, Nima Ghasemzadeh, Saeed Soltani and Marc A. Rosen
Sustainability 2024, 16(20), 8956; https://doi.org/10.3390/su16208956 - 16 Oct 2024
Abstract
Biomass is a viable and accessible source of energy that can help address the problem of energy shortages in rural and remote areas. Another important issue for societies today is the lack of clean water, especially in places with high populations and low [...] Read more.
Biomass is a viable and accessible source of energy that can help address the problem of energy shortages in rural and remote areas. Another important issue for societies today is the lack of clean water, especially in places with high populations and low rainfall. To address both of these concerns, a sustainable biomass-fueled power cycle integrated with a double-stage reverse osmosis water-desalination unit has been designed. The double-stage reverse osmosis system is provided by the 20% of generated power from the bottoming cycles and this allocation can be altered based on the needs for freshwater or power. This system is assessed from energy, exergy, thermoeconomic, and environmental perspectives, and two distinct multi-objective optimization scenarios are applied featuring various objective functions. The considered parameters for this assessment are gas turbine inlet temperature, compressor’s pressure ratio, and cold end temperature differences in heat exchangers 2 and 3. In the first optimization scenario, considering the pollution index, the total unit cost of exergy products, and exergy efficiency as objective functions, the optimal values are, respectively, identified as 0.7644 kg/kWh, 32.7 USD/GJ, and 44%. Conversely, in the second optimization scenario, featuring the emission index, total unit cost of exergy products, and output net power as objective functions, the optimal values are 0.7684 kg/kWh, 27.82 USD/GJ, and 2615.9 kW. Full article
Show Figures

Figure 1

Figure 1
<p>Schematic of the proposed system.</p>
Full article ">Figure 2
<p>Relationship between RP<sub>GTC</sub> and parameters <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">W</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">t</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mi>I</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">f</mi> <mi mathvariant="normal">w</mi> </mrow> </msub> </mrow> </semantics></math>, and SUCP.</p>
Full article ">Figure 3
<p>Relationship between RP<sub>SCO2</sub> and the parameters <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">W</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">t</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mi>I</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">f</mi> <mi mathvariant="normal">w</mi> </mrow> </msub> </mrow> </semantics></math>, and SUCP.</p>
Full article ">Figure 4
<p>Relationship between CETD<sub>HE2</sub> and four variables: W<sub>net</sub>, η, mfw, and SUCP.</p>
Full article ">Figure 5
<p>Relationship between CETD<sub>HE3</sub> and <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">W</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">t</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mi>I</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">f</mi> <mi mathvariant="normal">w</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 6
<p>Relationship between T<sub>3</sub> and SUCP, <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">W</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">n</mi> <mi mathvariant="normal">e</mi> <mi mathvariant="normal">t</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>η</mi> </mrow> <mrow> <mi>I</mi> <mi>I</mi> </mrow> </msub> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <msub> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mo>˙</mo> </mover> </mrow> <mrow> <mi mathvariant="normal">f</mi> <mi mathvariant="normal">w</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
Full article ">Figure 7
<p>(<b>a</b>) Pareto frontier for the first model multi-objective optimization. (<b>b</b>) Pareto frontier for the second model multi-objective optimization. The application of genetic algorithms to multi-objective optimization produced Pareto frontiers for both scenarios, as illustrated in (<b>a</b>,<b>b</b>). The proposed optimal values, taking into account three variables—ζ, cp, and exergy efficiency—are 0.7644 kg/kWh, 32.7 USD/GJ, and 44.12%, respectively. Moving to the second scenario, considering three variables—ζ, cp, and net output power—the values stand at 0.7684 kg/kWh, 27.823 USD/GJ, and 2615.90 kW, respectively.</p>
Full article ">Figure 7 Cont.
<p>(<b>a</b>) Pareto frontier for the first model multi-objective optimization. (<b>b</b>) Pareto frontier for the second model multi-objective optimization. The application of genetic algorithms to multi-objective optimization produced Pareto frontiers for both scenarios, as illustrated in (<b>a</b>,<b>b</b>). The proposed optimal values, taking into account three variables—ζ, cp, and exergy efficiency—are 0.7644 kg/kWh, 32.7 USD/GJ, and 44.12%, respectively. Moving to the second scenario, considering three variables—ζ, cp, and net output power—the values stand at 0.7684 kg/kWh, 27.823 USD/GJ, and 2615.90 kW, respectively.</p>
Full article ">Figure 8
<p>(<b>a</b>) Scattered distribution for RP<sub>SCO2</sub>. (<b>b</b>) Scattered distribution for CETD<sub>HE2</sub>. (<b>c</b>) Scattered distribution for CETD<sub>HE3</sub>. (<b>d</b>) Scattered distribution for T<sub>3</sub>. (<b>e</b>) Scattered distribution for RP<sub>GTC</sub>.</p>
Full article ">Figure 8 Cont.
<p>(<b>a</b>) Scattered distribution for RP<sub>SCO2</sub>. (<b>b</b>) Scattered distribution for CETD<sub>HE2</sub>. (<b>c</b>) Scattered distribution for CETD<sub>HE3</sub>. (<b>d</b>) Scattered distribution for T<sub>3</sub>. (<b>e</b>) Scattered distribution for RP<sub>GTC</sub>.</p>
Full article ">Figure 8 Cont.
<p>(<b>a</b>) Scattered distribution for RP<sub>SCO2</sub>. (<b>b</b>) Scattered distribution for CETD<sub>HE2</sub>. (<b>c</b>) Scattered distribution for CETD<sub>HE3</sub>. (<b>d</b>) Scattered distribution for T<sub>3</sub>. (<b>e</b>) Scattered distribution for RP<sub>GTC</sub>.</p>
Full article ">
18 pages, 16437 KiB  
Article
CFD Simulation of Mixing Forest Biomass to Obtain Cellulose
by Adolfo Angel Casarez-Duran, Juan Carlos Paredes-Rojas, Christopher René Torres-San Miguel, Sergio Rodrigo Méndez-García, Fernando Eli Ortiz-Hernández and Guillermo Manuel Urriolagoitia Calderón
Processes 2024, 12(10), 2250; https://doi.org/10.3390/pr12102250 (registering DOI) - 15 Oct 2024
Viewed by 191
Abstract
Obtaining cellulose from forest residues develops sustainable processes in the biotechnology industry, especially in producing biopolymers, which could replace or add petroleum-derived polymers. This research seeks to optimize the ideal conditions of the mixing process to maximize the efficiency in obtaining cellulose through [...] Read more.
Obtaining cellulose from forest residues develops sustainable processes in the biotechnology industry, especially in producing biopolymers, which could replace or add petroleum-derived polymers. This research seeks to optimize the ideal conditions of the mixing process to maximize the efficiency in obtaining cellulose through a process consisting of two treatment media for pine sawdust, specifically evaluating the impact of three types of impellers (propeller, flat blades, and 45° inclined flat blades) at speeds of (150, 250 and 350 rpm). DIN 28131 was used for the design of stirred tanks. Simulations were carried out with a volume of 50 L. CFD and FSI simulations of the agitation behavior of forest biomass in a stirred tank reactor were performed. The ALE method was applied, and the models were solved using the LS-DYNA computer program. The results indicate that agitation with propellers and flat blades inclined at 150 and 250 rpm was the most efficient, minimizing cell damage and optimizing energy consumption. The impeller with flat blades inclined at 45° proved to be the best option for cellulose extraction. The novelty of this research is that not only the flow fields and the agitation behavior were found, but also the stresses in the impellers were found, and the force, moment, and power required by the motor in each simulation were revealed at a different speed. The power curves shown help to understand how energy consumption varies under different conditions. Full article
Show Figures

Figure 1

Figure 1
<p>Methodology.</p>
Full article ">Figure 2
<p>Block diagram of each treatment.</p>
Full article ">Figure 3
<p>Three-dimensional modeling: (<b>a</b>) modeling with the propeller impeller; (<b>b</b>) modeling with the flat impeller; (<b>c</b>) modeling with the inclined flat-blade impeller.</p>
Full article ">Figure 4
<p>Boundary conditions of the impellers: (<b>a</b>) propeller; (<b>b</b>) flat blade; (<b>c</b>) inclined flat blade.</p>
Full article ">Figure 5
<p>Mix—biomass–alcohol: (<b>a</b>) power-vs.-RPM graph; (<b>b</b>) speed-stirring-vs.-RPM graph.</p>
Full article ">Figure 6
<p>Mix—biomass–sodium chlorite solution: (<b>a</b>) power-vs.-RPM graph; (<b>b</b>) speed-stirring-vs.- RPM graph.</p>
Full article ">
16 pages, 8467 KiB  
Article
Quality Enhancement of Torrefied Biopellets Prepared by Unused Forest Biomass and Wood Chip Residues in Pulp Mills
by Tae-Gyeong Lee, Chul-Hwan Kim, Hyeong-Hun Park, Ju-Hyun Park, Min-Sik Park and Jae-Sang Lee
Appl. Sci. 2024, 14(20), 9398; https://doi.org/10.3390/app14209398 (registering DOI) - 15 Oct 2024
Viewed by 348
Abstract
The effects of torrefaction of the biopellets made from hardwood chip residue (HW), camellia oilseed cake (CO), and pruning remnants of the toothache tree (TA) and mulberry tree (MT) were evaluated. Torrefaction of the biopellets reduced the volatile matter content of biopellets by [...] Read more.
The effects of torrefaction of the biopellets made from hardwood chip residue (HW), camellia oilseed cake (CO), and pruning remnants of the toothache tree (TA) and mulberry tree (MT) were evaluated. Torrefaction of the biopellets reduced the volatile matter content of biopellets by 18–58% and increased their heating value by 18–58% without negatively impacting durability or fines content. Torrefaction also reduced the initial ignition time of biopellets by 50–59% and prolonged their combustion duration by 15–24%. Regardless of the type of feedstock, all biopellets exhibited mass yields in the range of 60–80% and energy yields ranging from 80–95%. The novelty of this study lies in the application of torrefaction to already-formed biopellets, which enhances pellet quality without the need for binders, and the use of unused forest biomass and wood chip residue from pulp mills. The use of unused forest biomass and wood chip residue from pulp mills for biopellet production not only provides a sustainable and efficient method for waste utilization but also contributes to environmental conservation by reducing the reliance on fossil fuels. Overall, the torrefaction of biopellets represents a promising technology for producing high-quality solid biofuel from a variety of woody biomass feedstocks without compromising pelletizing efficiency. Full article
(This article belongs to the Section Applied Industrial Technologies)
Show Figures

Figure 1

Figure 1
<p>Ground woody biomass used to manufacture biopellets.</p>
Full article ">Figure 2
<p>Pelletizer with a flat die.</p>
Full article ">Figure 3
<p>Images of pellets before and after torrefaction using various raw materials.</p>
Full article ">Figure 4
<p>Experimental image of analyzing time for the initial ignition and combustion duration of biopellets: (<b>a</b>) pellet ignition by a potable gas torch; (<b>b</b>) ignited pellet with a flame.</p>
Full article ">Figure 5
<p>Proximate analysis of the prepared pellets before and after torrefaction.</p>
Full article ">Figure 6
<p>Ultimate analysis of the prepared pellets before and after torrefaction.</p>
Full article ">Figure 7
<p>Durability of the prepared pellets before and after torrefaction: (<b>a</b>) durability; (<b>b</b>) fines content.</p>
Full article ">Figure 8
<p>Bulk density of the prepared pallets before and after torrefaction.</p>
Full article ">Figure 9
<p>Calorific value of the prepared biopellets before and after torrefaction.</p>
Full article ">Figure 10
<p>Mass and energy yield of the prepared biopellets by torrefaction.</p>
Full article ">Figure 11
<p>Ignition and combustion time of the biopellets before and after torrefaction: (<b>a</b>) ignition time; (<b>b</b>) combustion duration.</p>
Full article ">Figure 12
<p>Thermogravimetric analysis of the biopellets before and after torrefaction: (<b>a</b>) HW; (<b>b</b>) CO; (<b>c</b>) TA; (<b>d</b>) MT.</p>
Full article ">Figure 13
<p>SEM images of biopellets before and after torrefaction.</p>
Full article ">Figure 14
<p>Importance and applications of torrefied biopellets.</p>
Full article ">
36 pages, 8178 KiB  
Article
Co-Inoculation of Soybean Seeds with Azospirillum and/or Rhizophagus Mitigates the Deleterious Effects of Waterlogging in Plants under Enhanced CO2 Concentrations
by Eduardo Pereira Shimoia, Douglas Antônio Posso, Cristiane Jovelina da-Silva, Adriano Udich Bester, Nathalia Dalla Corte Bernardi, Ivan Ricardo Carvalho, Ana Cláudia Barneche de Oliveira, Luis Antonio de Avila and Luciano do Amarante
Nitrogen 2024, 5(4), 941-976; https://doi.org/10.3390/nitrogen5040061 (registering DOI) - 15 Oct 2024
Viewed by 353
Abstract
Rising CO2 levels, as predicted by global climate models, are altering environmental factors such as the water cycle, leading to soil waterlogging and reduced oxygen availability for plant roots. These conditions result in decreased energy production, increased fermentative metabolism, impaired nutrient uptake, [...] Read more.
Rising CO2 levels, as predicted by global climate models, are altering environmental factors such as the water cycle, leading to soil waterlogging and reduced oxygen availability for plant roots. These conditions result in decreased energy production, increased fermentative metabolism, impaired nutrient uptake, reduced nitrogen fixation, and altered leaf gas exchanges, ultimately reducing crop productivity. Co-inoculation techniques involving multiple plant growth-promoting bacteria or arbuscular mycorrhizal fungi have shown promise in enhancing plant resilience to stress by improving nutrient uptake, biomass production, and nitrogen fixation. This study aimed to investigate carbon and nitrogen metabolism adaptations in soybean plants co-inoculated with Bradyrhizobium elkanii, Azospirillum brasilense, and Rhizophagus intraradices under waterlogged conditions in CO2-enriched environments. Plants were grown in pots in open-top chambers at ambient CO2 concentration (a[CO2]) and elevated CO2 concentration (e[CO2]). After reaching the V5 growth stage, the plants were subjected to waterlogging for seven days, followed by a four-day reoxygenation period. The results showed that plants’ co-inoculation under e[CO2] mitigated the adverse effects of waterlogging. Notably, plants inoculated solely with B. elkanii under e[CO2] displayed results similar to co-inoculated plants under a[CO2], suggesting that co-inoculation effectively mitigates the waterlogging stress, with plant physiological traits comparable to those observed under elevated CO2 conditions. Full article
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Figure 1

Figure 1
<p>Schematic representation of the treatments and experimental design. Soybean plants were cultivated under different CO<sub>2</sub> concentrations (ambient <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) and subsequent reoxygenation (four days). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple co-inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 2
<p>Leaf gaseous exchange. Net CO<sub>2</sub> assimilation (<span class="html-italic">A</span>) (<b>A</b>), stomatal conductance (<span class="html-italic">g<sub>s</sub></span>) (<b>B</b>), transpiration (<span class="html-italic">E</span>) (<b>C</b>), and internal CO<sub>2</sub> concentration (<span class="html-italic">C<sub>i</sub></span>) (<b>D</b>) in soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± standard deviation (SD), <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 3
<p>Pigment content. Chlorophyll <span class="html-italic">a</span> content (Chlo<span class="html-italic">_a</span>) (<b>A</b>), chlorophyll <span class="html-italic">b</span> (Chlo<span class="html-italic">_b</span>) (<b>B</b>), total chlorophyll (Chlo-total) (<b>C</b>), and carotenoids (Carot) (<b>D</b>) in soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 4
<p>Peroxide content and lipid peroxidation in leaves. Accumulation of hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) (<b>A</b>) and lipid peroxidation (MDA) (<b>B</b>) in leaves of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 5
<p>Peroxide content and lipid peroxidation in roots. Accumulation of hydrogen peroxide (H<sub>2</sub>O<sub>2</sub>) (<b>A</b>) and lipid peroxidation (MDA) (<b>B</b>) in roots of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 6
<p>Antioxidant enzyme activity in leaves. The activity of the enzymes superoxide dismutase (SOD) (<b>A</b>), catalase (CAT) (<b>B</b>), and ascorbate peroxidase (APX) (<b>C</b>) in leaves of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 7
<p>Antioxidant enzyme activity in roots. The activity of the enzymes superoxide dismutase (SOD) (<b>A</b>), catalase (CAT) (<b>B</b>), and ascorbate peroxidase (APX) (<b>C</b>) in roots of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 8
<p>Fermentative enzyme activity. Activity of the enzymes lactate dehydrogenase (LDH) (<b>A</b>), pyruvate decarboxylase (PDC) (<b>B</b>), alcohol dehydrogenase (ADH) (<b>C</b>), and alanine aminotransferase (Ala-At) (<b>D</b>) in roots of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 9
<p>Shoot biometric parameters. Leaf area (LA) (<b>A</b>), shoot dry mass (SDM) (<b>B</b>), and stem diameter (SD) (<b>C</b>) in soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 10
<p>Root biometric parameters. Accumulation of total soluble sugars (TSS) (<b>A</b>) and root fresh mass (RFM) (<b>B</b>) in roots of soybean plants grown under different CO<sub>2</sub> concentrations (ambient concentration <span class="html-italic">a</span>[CO<sub>2</sub>] or elevated concentration <span class="html-italic">e</span>[CO<sub>2</sub>]) with different symbiotic associations and subjected to waterlogging (seven days) followed by reoxygenation (four days). Values represent the mean ± SD, <span class="html-italic">n</span> = 4. Asterisks indicate a difference between control or waterlogged/reoxygenated plants (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05), uppercase letters indicate differences between treatments under control conditions, lowercase letters indicate differences between treatments under waterlogging/reoxygenated conditions (Tukey, <span class="html-italic">p</span> &lt; 0.05), and Greek letters indicate differences between treatment in <span class="html-italic">a</span>[CO<sub>2</sub>] or <span class="html-italic">e</span>[CO<sub>2</sub>] (<span class="html-italic">t</span>-test; <span class="html-italic">p</span> &lt; 0.05). IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation with <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation with <span class="html-italic">Rhizophagus intraradices</span> + <span class="html-italic">Bradyrhizobium</span>; CAR—triple inoculation with <span class="html-italic">Bradyrhizobium</span> + <span class="html-italic">Azospirillum brasilense</span> + <span class="html-italic">Rhizophagus intraradices</span>.</p>
Full article ">Figure 11
<p>Principal component analysis (PCA) was performed using PC1 and PC2 derived from morphophysiological and biochemical characteristics in the shoots of soybean plants grown under different symbiotic associations and subjected to waterlogging for seven days, followed by four days of reoxygenation, under either elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. I—first sampling during waterlogging; II—second sampling during reoxygenation; 400—plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]; 700—plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>]; Ctrl—plants maintained as hydric controls; Wtlg—plants subjected to waterlogging; Rox—plants undergoing reoxygenation; IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation of <span class="html-italic">Azospirillum brasilense</span> and <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation of <span class="html-italic">Rhizophagus intraradices</span> and <span class="html-italic">Bradyrhizobium</span>; CAR—triple co-inoculation of <span class="html-italic">Bradyrhizobium</span>, <span class="html-italic">Azospirillum brasilense</span>, and <span class="html-italic">Rhizophagus intraradices</span>. Ellipses of different colors delineate the 95% confidence intervals, with colors chosen according to the water treatment in each CO<sub>2</sub> environment. Different symbols represent the microbiological treatments.</p>
Full article ">Figure 12
<p>Principal component analysis (PCA) was performed using PC1 and PC2 derived from morphophysiological and biochemical characteristics in the roots of soybean plants grown under different symbiotic associations and subjected to waterlogging for seven days, followed by four days of reoxygenation, under either elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. I—first sampling during waterlogging; II—second sampling during reoxygenation; 400—plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]; 700—plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>]; Ctrl—plants maintained as hydric controls; Wtlg—plants subjected to waterlogging; Rox—plants undergoing reoxygenation; IB—<span class="html-italic">Bradyrhizobium</span> inoculation; CA—co-inoculation of <span class="html-italic">Azospirillum brasilense</span> and <span class="html-italic">Bradyrhizobium</span>; CR—co-inoculation of <span class="html-italic">Rhizophagus intraradices</span> and <span class="html-italic">Bradyrhizobium</span>; CAR—triple co-inoculation of <span class="html-italic">Bradyrhizobium</span>, <span class="html-italic">Azospirillum brasilense</span>, and <span class="html-italic">Rhizophagus intraradices</span>. Ellipses of different colors delineate the 95% confidence intervals, with colors chosen according to the water treatment in each CO<sub>2</sub> environment. Different symbols represent the microbiological treatments.</p>
Full article ">Figure 13
<p>Hierarchical clustering analysis (HCA) of H<sub>2</sub>O<sub>2</sub>, MDA, Chlo<span class="html-italic">_a</span>, Chlo<span class="html-italic">_b</span>, SOD, CAT, and APX enzyme activities, gas exchange parameters (<span class="html-italic">g<sub>s</sub></span>, <span class="html-italic">E</span>, <span class="html-italic">A</span>, and <span class="html-italic">C<sub>i</sub></span>), and biometric measurements (LA, SD, SDW) in the shoots of waterlogged soybean plants grown under elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. Variations in red and blue colors indicate increases and decreases, respectively, for each variable. Light gray shades represent control plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>], while dark gray shades represent waterlogged plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]. Red shades indicate control plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>], and blue shades represent waterlogged plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>].</p>
Full article ">Figure 14
<p>Hierarchical clustering analysis (HCA) of H<sub>2</sub>O<sub>2</sub>, MDA, Chlo<span class="html-italic">_a</span>, Chlo<span class="html-italic">_b</span>, SOD, CAT, and APX enzyme activities, gas exchange parameters (<span class="html-italic">g<sub>s</sub></span>, <span class="html-italic">E</span>, <span class="html-italic">A</span>, and <span class="html-italic">C<sub>i</sub></span>), and biometric measurements (LA, SD, SDW) in the shoots of reoxygenated soybean plants grown under elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. Variations in red and blue colors indicate increases and decreases, respectively, for each variable. Light gray shades represent control plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>], while dark gray shades represent waterlogged plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]. Red shades indicate control plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>], and blue shades represent waterlogged plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>].</p>
Full article ">Figure 15
<p>Hierarchical clustering analysis (HCA) of H<sub>2</sub>O<sub>2</sub>, MDA, TSS, SOD, CAT, APX, ADH, LDH, PDC, and Ala-AT enzyme activities, as well as RFW in the roots of waterlogged soybean plants grown under elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. Variations plotted in red and blue colors on a log10 scale indicate increases and decreases, respectively, for each variable. Light gray shades represent control plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>], while dark gray shades denote waterlogged plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]. Red shades indicate control plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>], and blue shades represent waterlogged plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>].</p>
Full article ">Figure 16
<p>Hierarchical clustering analysis (HCA) of H<sub>2</sub>O<sub>2</sub>, MDA, TSS, SOD, CAT, APX, ADH, LDH, PDC, and Ala-AT enzyme activities, as well as RFW in the roots of reoxygenated soybean plants grown under elevated CO<sub>2</sub> (<span class="html-italic">e</span>[CO<sub>2</sub>]) or ambient CO<sub>2</sub> (<span class="html-italic">a</span>[CO<sub>2</sub>]) conditions. Variations plotted in red and blue colors on a log10 scale indicate increases and decreases, respectively, for each variable. Light gray shades represent control plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>], while dark gray shades denote waterlogged plants grown under <span class="html-italic">a</span>[CO<sub>2</sub>]. Red shades indicate control plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>], and blue shades represent waterlogged plants grown under <span class="html-italic">e</span>[CO<sub>2</sub>].</p>
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15 pages, 2203 KiB  
Article
Investigating the Potential Use of End-of-Life Fire Extinguisher Powder as a Soil Amendment in Different Soil Types: A New Approach Following a Circular Economy Model
by Ioanna Tsigka, Nikolaos Kiatikidis, Panagiotis K. Tsolakis, Nikolaos Stergiou and Evangelia E. Golia
Sustainability 2024, 16(20), 8913; https://doi.org/10.3390/su16208913 - 15 Oct 2024
Viewed by 398
Abstract
A first attempt to assess the potential alternative use of fire extinguisher filler powder after its exhaustion has been investigated in the present research. The chemical composition of fire extinguisher filler powder, specifically type ABC 40%, consists of monoammonium phosphate and ammonium sulfate. [...] Read more.
A first attempt to assess the potential alternative use of fire extinguisher filler powder after its exhaustion has been investigated in the present research. The chemical composition of fire extinguisher filler powder, specifically type ABC 40%, consists of monoammonium phosphate and ammonium sulfate. As its nitrogen and phosphorus content is particularly high, the thought of its possible use as a fertilizer and/or a soil amendment is a challenge. For this purpose, a pot experiment was carried out and two leafy vegetables (spinach and lettuce) were used as biomarkers. Two soil samples from rural areas, one acidic (pH = 5.8 ± 0.1) and one alkaline (pH = 8.2 ± 0.7), were selected for the experiments. Filler powder from a used fire extinguisher was added to the soil samples in two levels (1 and 2% v/v). It was found that the addition of fire extinguisher filler powder caused no toxicity to either of the two plants studied. On the contrary, an increase in their above-ground biomass was observed, proportional to the amount of powder added. It was established that in the pots where the powder was added, in both plant species observed, the plant height, root length, and chlorophyll content of leaves increased, the total antioxidant capacity was enhanced, and the concentrations of nitrate and phosphate in the leaves and roots of plants also increased, compared to the soil without the addition of fire extinguisher powder. The early signs appear to be encouraging, as an increase was observed in almost all aspects. The mandatory end of the life cycle of the powder as a fire-extinguishing agent and its disposal is also a challenge in the context of the circular economy, as reducing the energy requirements for fertilizer production is one of the objectives of sustainable development. Full article
(This article belongs to the Special Issue Recycling Materials for the Circular Economy—2nd Edition)
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<p>Spinach (<span class="html-italic">S. oleraceae</span>) and lettuce (<span class="html-italic">L. sativa</span>) height variations after the addition of two levels of fire extinguisher filler powder to soil samples (S1: acidic and S2: alkaline), (capital letters show the significant differences between the treatments of lettuce, while lowercase letters show the significant differences between the treatments of spinach. Error bars indicate standard deviation, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 2
<p>Spinach (<span class="html-italic">S. oleraceae</span>) and lettuce (<span class="html-italic">L. sativa</span>) roots length (cm) after the addition of two levels of fire extinguisher filler powder to soil samples (S1: acidic and S2: alkaline), (capital letters show the significant differences between the treatments of lettuce, while lowercase letters show the significant differences between the treatments of spinach. Error bars indicate standard deviation, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 3
<p>Chlorophyll content expressed as SPAD values of spinach (<span class="html-italic">S. oleraceae</span>) and lettuce (<span class="html-italic">L. sativa</span>) with the addition of two different levels of fire extinguisher filler powders in acidic (S1) and alkaline soils (S2) (capital letters show the significant differences between the treatments of lettuce, while lowercase letters show the significant differences between the treatments of spinach. Error bars indicate standard deviation, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 4
<p>The total antioxidant activity (AA) (µmol Trolox equivalents (TE) g<sup>-1</sup>) of spinach (<span class="html-italic">S. oleraceae</span>) and lettuce (<span class="html-italic">L. sativa</span>) with the addition of two different levels of fire extinguisher filler powders in acidic (S1) and alkaline soils (S2) (capital letters show the significant differences between the treatments of lettuce, while lowercase letters show the significant differences between the treatments of spinach. Error bars indicate standard deviation, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 5
<p>Nitrates concentration in the leaves and roots of spinach (<span class="html-italic">S. oleraceae</span>) and lettuce (<span class="html-italic">L. sativa</span>) with the addition of two different levels of fire extinguisher filler powders in acidic (S1) and alkaline soils (S2) (capital letters show the significant differences between the treatments of lettuce, while lowercase letters show the significant differences between the treatments of spinach. Error bars indicate standard deviation, <span class="html-italic">p</span> &lt; 0.05).</p>
Full article ">Figure 6
<p>Phosphates concentration in the leaves and roots of spinach (<span class="html-italic">S. oleraceae</span>) and lettuce (<span class="html-italic">L. sativa</span>) with the addition of two different levels of fire extinguisher filler powders in acidic (S1) and alkaline soils (S2) (capital letters show the significant differences between the treatments of lettuce, while lowercase letters show the significant differences between the treatments of spinach. Error bars indicate standard deviation, <span class="html-italic">p</span> &lt; 0.05).</p>
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28 pages, 1353 KiB  
Review
Solar Organic Rankine Cycle (ORC) Systems: A Review of Technologies, Parameters, and Applications
by Dominika Matuszewska
Energies 2024, 17(20), 5106; https://doi.org/10.3390/en17205106 (registering DOI) - 14 Oct 2024
Viewed by 322
Abstract
The Organic Rankine Cycle (ORC) is a widely utilized technology for generating electricity from various sources, including geothermal energy, waste heat, biomass, and solar energy. Harnessing solar radiation to drive ORC is a promising renewable energy technology due to the high compatibility of [...] Read more.
The Organic Rankine Cycle (ORC) is a widely utilized technology for generating electricity from various sources, including geothermal energy, waste heat, biomass, and solar energy. Harnessing solar radiation to drive ORC is a promising renewable energy technology due to the high compatibility of solar collector operating temperatures with the thermal requirements of the cycle. The aim of this review article is to present and discuss the principles of solar-ORC technology and the broad range of solar-ORC systems that have been explored in the literature. Various solar energy technologies capable of powering ORC are investigated, including flat plate collectors, vacuum tube collectors, compound parabolic collectors, and parabolic trough collectors. The review places significant emphasis on the operating parameters of technology. Full article
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<p>Basic ORC system with key component and state-point notations.</p>
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<p>Temperature-specific entropy diagram of an ORC system.</p>
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<p>Temperature–heat diagram for preheater, evaporator, and superheater.</p>
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<p>Some of the wet, isentropic, and dry fluids using in ORCs.</p>
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<p>Temperature ranges typical for different solar thermal collectors [<a href="#B54-energies-17-05106" class="html-bibr">54</a>].</p>
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<p>Schematic layout of a direct solar-ORC system.</p>
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<p>Schematic layout of indirect solar-ORC system.</p>
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17 pages, 1529 KiB  
Article
Post-Extractivism and Bioeconomy: An Experimental Analysis of Combustion and Pyrolysis Processes as Alternatives to Add Value to Agro-Residues (Coffee Husks) Generated in Farmer Cooperatives of the Ecuadorian Amazon
by Mario A. Heredia Salgado, Ina Säumel and Luís A. C. Tarelho
Resources 2024, 13(10), 142; https://doi.org/10.3390/resources13100142 - 14 Oct 2024
Viewed by 259
Abstract
A post-extractivist development model for communities in the Amazon that is not based on non-renewable resource extraction demands the study and demonstration, in the field, of alternative economic activities that add value to currently generated residual biomass. Following the principles of bioeconomy, this [...] Read more.
A post-extractivist development model for communities in the Amazon that is not based on non-renewable resource extraction demands the study and demonstration, in the field, of alternative economic activities that add value to currently generated residual biomass. Following the principles of bioeconomy, this study presents an experimental analysis of a retort burner and a pilot-scale auger-type pyrolysis reactor used to convert coffee husks generated in a collection and post-harvesting center of a farmer’s cooperative into thermal energy and biochar, respectively. This study shows that coffee husks, whether used as feedstock for combustion or pyrolysis processes, can supply the thermal energy required by the post-harvesting processes. The combustion or pyrolysis of coffee husks avoids its accumulation and decomposition while replacing fossil fuels used in post-harvesting operations, reducing costs and making farmers independent of fossil fuel subsidies. Unlike combustion (11,029.4 mg/Nm3), the CO concentration in the flue gas during the pyrolysis process was 458.3 mg/Nm3, which is below the eco-design standard of 500 mg/Nm3. According to the European Biochar Certificate, carbon content (67.4 wt%) and H/Corg, O/Corg (0.6 and 0.1, respectively) are within the typical values of biochars used for soil amendment and carbon sequestration. Nonetheless, the concentration of polycyclic aromatic hydrocarbons must be assessed to fully regard this material as biochar. Finally, further studies are required to assess the ability of cooperatives to generate and trade carbon credits linked with the application of biochar in their cropping systems. Full article
(This article belongs to the Special Issue Resource Extraction from Agricultural Products/Waste: 2nd Edition)
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<p>Retort burner used in combustion experiments. Feedstock: coffee husks. The height, width, and length of the retort burner (including the supporting legs) are 2.0 m × 1.2 m × 2.5 m, respectively.</p>
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<p>Pilot-scale auger-type pyrolysis reactor used for the pyrolysis experiments. Feedstock: coffee husks. Length 1.7 m. Diameter 0.65 m. Thickness insulation: 0.15 m.</p>
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<p>Schematic diagram of the sampling and conditioning processes of the combustion flue gas implemented during combustion experiments using coffee husks in a retort burner. The diagram also shows the temperature monitoring points and the data logger system.</p>
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<p>Alternatives considered in this study to add value from the coffee husks generated in the farmer cooperative APROCEL through thermochemical conversion processes, namely combustion (<b>A</b>), and pyrolysis (<b>B</b>).</p>
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<p>Large pieces of sintered ash extracted from the combustion bed of the retort burner after 60 min of operation, during combustion of raw coffee husks.</p>
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17 pages, 2855 KiB  
Article
Emergy-Based Evaluation of Xiaolangdi Reservoir’s Impact on the Ecosystem Health and Services of the Lower Yellow River
by Xiangping Zhang, Yuanjian Wang, Junhua Li, Yanhui Zhang and Shuping Zhang
Sustainability 2024, 16(20), 8857; https://doi.org/10.3390/su16208857 - 13 Oct 2024
Viewed by 455
Abstract
The disturbance in river ecosystems caused by reservoirs and dams has become a critical topic, attracting increasing attention. However, the extent to which reservoir and dam construction and operation impact downstream river ecosystem health and ecosystem service functions is not fully understood. This [...] Read more.
The disturbance in river ecosystems caused by reservoirs and dams has become a critical topic, attracting increasing attention. However, the extent to which reservoir and dam construction and operation impact downstream river ecosystem health and ecosystem service functions is not fully understood. This research examines the Xiaolangdi Reservoir and the Lower Yellow River (LYR) ecosystem in China as a case study. We analyzed the complex material and energy flows in the LYR ecosystem using emergy theory and developed a set of emergy-based indicators for the quantitative assessment of river ecosystem health and services under reservoir operation interference. The results indicate that the total natural capital and environmental endowments of the LYR ecosystem have remained relatively stable after the operation of the Xiaolangdi Reservoir, with an increase in renewable emergy input. The ecosystem’s vigor decreased slightly, while the biomass emergy diversity index remained stable. However, the total emergy inputs increased significantly, with external feedback inputs becoming the most important emergy source for the LYR ecosystem. The resilience of the LYR ecosystem improved, with a significant increase in emergy density and a decrease in the emergy sustainability index. These findings suggest that although the river ecosystem continues to provide supporting services to human society, the extent of these services has diminished compared to pre-perturbation levels. In this research, a methodology for analyzing the impact of key reservoir operations on the ecosystem health and services of a large river is proposed to provide support for large river sustainable development studies. Full article
(This article belongs to the Special Issue Hydrosystems Engineering and Water Resource Management)
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<p>Location of the Lower Yellow River and Xiaolangdi Reservoir in China. (<b>a</b>) The Yellow River basin; (<b>b</b>) The location of the floodplain in the LYR; (<b>c</b>) The structure of the floodplain in the LYR; (<b>d</b>) The floodplain of the LYR.</p>
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<p>The annual average amount of runoff and sediment into the LYR from 1986AD to 2022AD. (<b>a</b>) Runoff; (<b>b</b>) sediment. The data come from Hydrological data of Yellow River Basin from 1986 to 2022 [<a href="#B44-sustainability-16-08857" class="html-bibr">44</a>].</p>
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<p>Riverbed sediment grain size change in the LYR after the operation of Xiaolangdi Reservoir began. (<b>a</b>) Huayuankou; (<b>b</b>) Gaocun; (<b>c</b>) Lijin. The data come from Hydrological data of Yellow River Basin from 1999 to 2022 [<a href="#B44-sustainability-16-08857" class="html-bibr">44</a>].</p>
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<p>Flowchart of the methodology.</p>
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<p>Emergy system diagram of the LYR in China.</p>
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<p>Emergy-based river ecosystem health and services indicators.</p>
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<p>Summary diagram of emergy analysis for LYR ecosystem before and after the operation of Xiaolangdi Reservoir began. (<b>a</b>) Before the operation of Xiaolangdi Reservoir began; (<b>b</b>) after the operation of Xiaolangdi Reservoir began.</p>
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<p>The result of river ecosystem health indicators of the LYR before and after the operation of the Xiaolangdi Reservoir began. (<b>a</b>) Vigor; (<b>b</b>) organization; (<b>c</b>) resilience.</p>
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<p>The result of river ecosystem service function indicators of the LYR before and after the operation of the Xiaolangdi Reservoir began. (<b>a</b>) Ecosystem service function maintenance; (<b>b</b>) environmental impact; (<b>c</b>) environmental capacity.</p>
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31 pages, 2260 KiB  
Review
Comprehensive Review of Biomass Pyrolysis: Conventional and Advanced Technologies, Reactor Designs, Product Compositions and Yields, and Techno-Economic Analysis
by Wojciech Jerzak, Esther Acha and Bin Li
Energies 2024, 17(20), 5082; https://doi.org/10.3390/en17205082 - 12 Oct 2024
Viewed by 667
Abstract
Pyrolysis is an environmentally friendly and efficient method for converting biomass into a wide range of products, including fuels, chemicals, fertilizers, catalysts, and sorption materials. This review confirms that scientific research on biomass pyrolysis has remained strong over the past 10 years. The [...] Read more.
Pyrolysis is an environmentally friendly and efficient method for converting biomass into a wide range of products, including fuels, chemicals, fertilizers, catalysts, and sorption materials. This review confirms that scientific research on biomass pyrolysis has remained strong over the past 10 years. The authors examine the operating conditions of different types of pyrolysis, including slow, intermediate, fast, and flash, highlighting the distinct heating rates for each. Furthermore, biomass pyrolysis reactors are categorized into four groups, pneumatic bed reactors, gravity reactors, stationary bed reactors, and mechanical reactors, with a discussion on each type. The review then focuses on recent advancements in pyrolysis technologies that have improved efficiency, yield, and product quality, which, in turn, support sustainable energy production and effective waste management. The composition and yields of products from the different types of pyrolysis have been also reviewed. Finally, a techno-economic analysis has been conducted for both the pyrolysis of biomass alone and the co-pyrolysis of biomass with other raw materials. Full article
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<p>Number of publications indexed in the Scopus database in the years 2014 to 2023 for searches within ‘biomass’ and ‘pyrolysis’ reported in titles, abstracts, and keywords; analysis performed on 10 June 2024.</p>
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<p>Biomass pyrolysis routes based on data from <a href="#energies-17-05082-t003" class="html-table">Table 3</a>.</p>
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<p>Types of reactors used for biomass pyrolysis.</p>
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<p>Pneumatic bed reactors: (<b>a</b>) bubbling fluidized bed, (<b>b</b>) conical spouted, (<b>c</b>) circulating fluidized bed, and (<b>d</b>) entrained flow.</p>
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<p>Free-fall reactor diagram.</p>
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<p>Stationary bed reactors: (<b>a</b>) batch, (<b>b</b>) semi-batch, (<b>c</b>) continuous fixed bed, and (<b>d</b>) fixed bed reactor in cyclic.</p>
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<p>Mechanical reactors: (<b>a</b>) rotating cone, (<b>b</b>) stirred bed, (<b>c</b>) auger, (<b>d</b>) ablative, and (<b>e</b>) rotary kiln.</p>
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<p>Bio-oil obtained in the pyrolysis process of various types of biomasses at a temperature of 500 °C, based on the literature: (<b>a</b>) yield [<a href="#B34-energies-17-05082" class="html-bibr">34</a>,<a href="#B35-energies-17-05082" class="html-bibr">35</a>,<a href="#B40-energies-17-05082" class="html-bibr">40</a>,<a href="#B42-energies-17-05082" class="html-bibr">42</a>,<a href="#B43-energies-17-05082" class="html-bibr">43</a>,<a href="#B45-energies-17-05082" class="html-bibr">45</a>,<a href="#B46-energies-17-05082" class="html-bibr">46</a>,<a href="#B49-energies-17-05082" class="html-bibr">49</a>,<a href="#B50-energies-17-05082" class="html-bibr">50</a>,<a href="#B52-energies-17-05082" class="html-bibr">52</a>,<a href="#B53-energies-17-05082" class="html-bibr">53</a>,<a href="#B54-energies-17-05082" class="html-bibr">54</a>,<a href="#B55-energies-17-05082" class="html-bibr">55</a>,<a href="#B58-energies-17-05082" class="html-bibr">58</a>,<a href="#B59-energies-17-05082" class="html-bibr">59</a>,<a href="#B61-energies-17-05082" class="html-bibr">61</a>,<a href="#B62-energies-17-05082" class="html-bibr">62</a>,<a href="#B63-energies-17-05082" class="html-bibr">63</a>,<a href="#B64-energies-17-05082" class="html-bibr">64</a>,<a href="#B65-energies-17-05082" class="html-bibr">65</a>,<a href="#B66-energies-17-05082" class="html-bibr">66</a>,<a href="#B68-energies-17-05082" class="html-bibr">68</a>,<a href="#B73-energies-17-05082" class="html-bibr">73</a>,<a href="#B74-energies-17-05082" class="html-bibr">74</a>], (<b>b</b>) O:C and H:C molar ratios and higher heating value [<a href="#B35-energies-17-05082" class="html-bibr">35</a>,<a href="#B40-energies-17-05082" class="html-bibr">40</a>,<a href="#B45-energies-17-05082" class="html-bibr">45</a>,<a href="#B49-energies-17-05082" class="html-bibr">49</a>,<a href="#B50-energies-17-05082" class="html-bibr">50</a>,<a href="#B51-energies-17-05082" class="html-bibr">51</a>,<a href="#B54-energies-17-05082" class="html-bibr">54</a>,<a href="#B59-energies-17-05082" class="html-bibr">59</a>,<a href="#B62-energies-17-05082" class="html-bibr">62</a>,<a href="#B74-energies-17-05082" class="html-bibr">74</a>], and (<b>c</b>) identified main functional groups [<a href="#B35-energies-17-05082" class="html-bibr">35</a>,<a href="#B40-energies-17-05082" class="html-bibr">40</a>,<a href="#B45-energies-17-05082" class="html-bibr">45</a>,<a href="#B49-energies-17-05082" class="html-bibr">49</a>,<a href="#B51-energies-17-05082" class="html-bibr">51</a>,<a href="#B52-energies-17-05082" class="html-bibr">52</a>,<a href="#B61-energies-17-05082" class="html-bibr">61</a>,<a href="#B71-energies-17-05082" class="html-bibr">71</a>,<a href="#B76-energies-17-05082" class="html-bibr">76</a>].</p>
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<p>Char obtained in the pyrolysis process of various types of biomasses at a temperature of 500 °C, based on the literature: (<b>a</b>) yield [<a href="#B34-energies-17-05082" class="html-bibr">34</a>,<a href="#B35-energies-17-05082" class="html-bibr">35</a>,<a href="#B40-energies-17-05082" class="html-bibr">40</a>,<a href="#B42-energies-17-05082" class="html-bibr">42</a>,<a href="#B43-energies-17-05082" class="html-bibr">43</a>,<a href="#B45-energies-17-05082" class="html-bibr">45</a>,<a href="#B46-energies-17-05082" class="html-bibr">46</a>,<a href="#B49-energies-17-05082" class="html-bibr">49</a>,<a href="#B50-energies-17-05082" class="html-bibr">50</a>,<a href="#B52-energies-17-05082" class="html-bibr">52</a>,<a href="#B53-energies-17-05082" class="html-bibr">53</a>,<a href="#B54-energies-17-05082" class="html-bibr">54</a>,<a href="#B55-energies-17-05082" class="html-bibr">55</a>,<a href="#B59-energies-17-05082" class="html-bibr">59</a>,<a href="#B61-energies-17-05082" class="html-bibr">61</a>,<a href="#B62-energies-17-05082" class="html-bibr">62</a>,<a href="#B63-energies-17-05082" class="html-bibr">63</a>,<a href="#B64-energies-17-05082" class="html-bibr">64</a>,<a href="#B65-energies-17-05082" class="html-bibr">65</a>,<a href="#B66-energies-17-05082" class="html-bibr">66</a>,<a href="#B68-energies-17-05082" class="html-bibr">68</a>,<a href="#B73-energies-17-05082" class="html-bibr">73</a>,<a href="#B74-energies-17-05082" class="html-bibr">74</a>] and (<b>b</b>) O:C and H:C molar ratios [<a href="#B34-energies-17-05082" class="html-bibr">34</a>,<a href="#B40-energies-17-05082" class="html-bibr">40</a>,<a href="#B43-energies-17-05082" class="html-bibr">43</a>,<a href="#B45-energies-17-05082" class="html-bibr">45</a>,<a href="#B49-energies-17-05082" class="html-bibr">49</a>,<a href="#B51-energies-17-05082" class="html-bibr">51</a>,<a href="#B54-energies-17-05082" class="html-bibr">54</a>,<a href="#B55-energies-17-05082" class="html-bibr">55</a>,<a href="#B59-energies-17-05082" class="html-bibr">59</a>,<a href="#B61-energies-17-05082" class="html-bibr">61</a>,<a href="#B66-energies-17-05082" class="html-bibr">66</a>,<a href="#B68-energies-17-05082" class="html-bibr">68</a>,<a href="#B74-energies-17-05082" class="html-bibr">74</a>].</p>
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<p>Non-condensable gas obtained in the pyrolysis process of various types of biomasses at a temperature of 500 °C, based on the literature: (<b>a</b>) yield [<a href="#B34-energies-17-05082" class="html-bibr">34</a>,<a href="#B35-energies-17-05082" class="html-bibr">35</a>,<a href="#B40-energies-17-05082" class="html-bibr">40</a>,<a href="#B42-energies-17-05082" class="html-bibr">42</a>,<a href="#B43-energies-17-05082" class="html-bibr">43</a>,<a href="#B45-energies-17-05082" class="html-bibr">45</a>,<a href="#B46-energies-17-05082" class="html-bibr">46</a>,<a href="#B49-energies-17-05082" class="html-bibr">49</a>,<a href="#B50-energies-17-05082" class="html-bibr">50</a>,<a href="#B52-energies-17-05082" class="html-bibr">52</a>,<a href="#B53-energies-17-05082" class="html-bibr">53</a>,<a href="#B54-energies-17-05082" class="html-bibr">54</a>,<a href="#B55-energies-17-05082" class="html-bibr">55</a>,<a href="#B59-energies-17-05082" class="html-bibr">59</a>,<a href="#B61-energies-17-05082" class="html-bibr">61</a>,<a href="#B62-energies-17-05082" class="html-bibr">62</a>,<a href="#B63-energies-17-05082" class="html-bibr">63</a>,<a href="#B64-energies-17-05082" class="html-bibr">64</a>,<a href="#B65-energies-17-05082" class="html-bibr">65</a>,<a href="#B66-energies-17-05082" class="html-bibr">66</a>,<a href="#B68-energies-17-05082" class="html-bibr">68</a>,<a href="#B73-energies-17-05082" class="html-bibr">73</a>,<a href="#B74-energies-17-05082" class="html-bibr">74</a>] and (<b>b</b>) content of hydrogen, methane, carbon dioxide, and carbon monoxide [<a href="#B35-energies-17-05082" class="html-bibr">35</a>,<a href="#B40-energies-17-05082" class="html-bibr">40</a>,<a href="#B43-energies-17-05082" class="html-bibr">43</a>,<a href="#B46-energies-17-05082" class="html-bibr">46</a>,<a href="#B51-energies-17-05082" class="html-bibr">51</a>,<a href="#B52-energies-17-05082" class="html-bibr">52</a>,<a href="#B63-energies-17-05082" class="html-bibr">63</a>,<a href="#B66-energies-17-05082" class="html-bibr">66</a>,<a href="#B73-energies-17-05082" class="html-bibr">73</a>,<a href="#B74-energies-17-05082" class="html-bibr">74</a>].</p>
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26 pages, 5090 KiB  
Article
Analysis and Optimization of a s-CO2 Cycle Coupled to Solar, Biomass, and Geothermal Energy Technologies
by Orlando Anaya-Reyes, Iván Salgado-Transito, David Aarón Rodríguez-Alejandro, Alejandro Zaleta-Aguilar, Carlos Benito Martínez-Pérez and Sergio Cano-Andrade
Energies 2024, 17(20), 5077; https://doi.org/10.3390/en17205077 - 12 Oct 2024
Viewed by 310
Abstract
This paper presents an analysis and optimization of a polygeneration power-production system that integrates a concentrating solar tower, a supercritical CO2 Brayton cycle, a double-flash geothermal Rankine cycle, and an internal combustion engine. The concentrating solar tower is analyzed under the weather [...] Read more.
This paper presents an analysis and optimization of a polygeneration power-production system that integrates a concentrating solar tower, a supercritical CO2 Brayton cycle, a double-flash geothermal Rankine cycle, and an internal combustion engine. The concentrating solar tower is analyzed under the weather conditions of the Mexicali Valley, Mexico, optimizing the incident radiation on the receiver and its size, the tower height, and the number of heliostats and their distribution. The integrated polygeneration system is studied by first and second law analyses, and its optimization is also developed. Results show that the optimal parameters for the solar field are a solar flux of 549.2 kW/m2, a height tower of 73.71 m, an external receiver of 1.86 m height with a 6.91 m diameter, and a total of 1116 heliostats of 6 m × 6 m. For the integrated polygeneration system, the optimal values of the variables considered are 1437 kPa and 351.2 kPa for the separation pressures of both flash chambers, 753 °C for the gasification temperature, 741.1 °C for the inlet temperature to the turbine, 2.5 and 1.503 for the turbine pressure ratios, 0.5964 for the air–biomass equivalence ratio, and 0.5881 for the CO2 mass flow splitting fraction. Finally, for the optimal system, the thermal efficiency is 38.8%, and the exergetic efficiency is 30.9%. Full article
(This article belongs to the Section B2: Clean Energy)
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<p>Schematic diagram of the integrated polygeneration power system under study.</p>
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<p>DNI observed in the Mexicali Valley, Mexico [<a href="#B36-energies-17-05077" class="html-bibr">36</a>].</p>
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<p>Cosine efficiency of the heliostat field.</p>
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<p>Optical efficiency of the heliostat field.</p>
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<p>Solar flux profile at the receiver.</p>
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<p>Effect of the gasification temperature on the syngas LHV and production rate m<sup>3</sup> of syngas/kg of biomass.</p>
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<p>Effect of the gasification temperature on the first and second law efficiencies of the gasification system.</p>
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<p>Effect of <math display="inline"><semantics> <mi>ξ</mi> </semantics></math> on the syngas LHV and production rate of the gasification system.</p>
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<p>Effect of <math display="inline"><semantics> <mi>ξ</mi> </semantics></math> on the energetic and exergetic efficiencies of the gasification system.</p>
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<p>Effect of <math display="inline"><semantics> <msub> <mi>T</mi> <mi>GTI</mi> </msub> </semantics></math> on the energetic and exergetic efficiencies of the Brayton cycle.</p>
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<p>Effect of <math display="inline"><semantics> <mi>γ</mi> </semantics></math> on the energetic and exergetic efficiencies of the Brayton cycle.</p>
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<p>Effect of <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mn>1</mn> </msub> </semantics></math> on the energetic and exergetic efficiencies of the Brayton cycle.</p>
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<p>Effect of <math display="inline"><semantics> <msub> <mi>ϕ</mi> <mn>2</mn> </msub> </semantics></math> on the energetic and exergetic efficiencies of the Brayton cycle.</p>
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<p>Effect of <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>sep</mi> <mn>1</mn> </mrow> </msub> </mrow> </semantics></math> on the energetic and exergetic efficiencies of the Rankine cycle.</p>
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<p>Effect of <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>sep</mi> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math> on the energetic and exergetic efficiencies of the Rankine cycle.</p>
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<p>Percentage of exergy destruction rates by component.</p>
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15 pages, 1795 KiB  
Article
Enhancing Sewage Sludge Stabilization, Pathogen Removal, and Biomass Production through Indigenous Microalgae Promoting Growth: A Sustainable Approach for Sewage Sludge Treatment
by Hajer Ben Hamed, Antoine Debuigne, Hetty Kleinjan, Dominique Toye and Angélique Léonard
Recycling 2024, 9(5), 97; https://doi.org/10.3390/recycling9050097 (registering DOI) - 12 Oct 2024
Viewed by 537
Abstract
Sewage sludge (SS), a byproduct of wastewater treatment plants, poses significant environmental and health risks if not properly handled. Conventional approaches for SS stabilization often involve costly and energy-consuming processes. This study investigated the effect of promoting native microalgae growth in SS on [...] Read more.
Sewage sludge (SS), a byproduct of wastewater treatment plants, poses significant environmental and health risks if not properly handled. Conventional approaches for SS stabilization often involve costly and energy-consuming processes. This study investigated the effect of promoting native microalgae growth in SS on its stabilization, pathogen bacteria removal, and valuable biomass production. The effect on settleability, filterability, and extracellular polymeric substances (EPSs) was examined as well. Experiments were conducted in photobioreactors (PBRs) without O2 supply and CO2 release under controlled parameters. The results show a significant improvement in SS stabilization, with a reduction of volatile solids (VSs) by 47.55%. Additionally, fecal coliforms and E. coli were efficiently removed by 2.25 log and 6.72 log, respectively. Moreover, Salmonella spp. was not detected after 15 days of treatment. The settleability was improved by 71.42%. However, a worsening of the sludge filterability properties was observed, likely due to a decrease in floc size following the reduction of protein content in the tightly bound EPS fraction. Microalgae biomass production was 16.56 mg/L/day, with a mean biomass of 0.35 g/L at the end of the batch treatment, representing 10.35% of the total final biomass. These findings suggest that promoting native microalgal growth in SS could be sustainable and cost-effective for SS stabilization, microalgal biomass production, and the enhancement of sludge-settling characteristics, notwithstanding potential filtration-related considerations. Full article
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Graphical abstract
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<p>Evolution of the total biomass, biomass fraction of microalgae, and sludge during the treatment.</p>
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<p>pH and DO change during the treatment process.</p>
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<p>TS and VS reduction during treatment.</p>
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<p>(<b>a</b>) Floc size and (<b>b</b>) SVI variation during treatment. RS: Raw Sludge.</p>
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<p>Filtrate volume versus filtration time for the sludge after different treatment durations. RS: Raw Sludge.</p>
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<p>Microscopy images of the mixture in the PBRs (40×): on the left, from the initial day of treatment; on the right, from the end of treatment (20 days). Depicted scale bars measure 50 µm in length.</p>
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<p>Effects of the process on EPS dynamics at different times. (<b>a</b>) The changes in protein content, (<b>b</b>) polysaccharide content, and (<b>c</b>) total EPS content in different fractions.</p>
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7 pages, 1710 KiB  
Opinion
Determining the Benefits of Biomass: Who Wins, and Who Loses?
by Daniel Taylor, Joanna Sparks, Katie Chong and Mirjam Röder
Agronomy 2024, 14(10), 2350; https://doi.org/10.3390/agronomy14102350 - 11 Oct 2024
Viewed by 344
Abstract
Beyond the technical challenge of using biomass to achieve net zero, non-technical factors also impact the likelihood of biomass succeeding in displacing fossil fuel use, such as social, environmental, and economic challenges. The political bioeconomy in the United Kingdom (UK) has supported a [...] Read more.
Beyond the technical challenge of using biomass to achieve net zero, non-technical factors also impact the likelihood of biomass succeeding in displacing fossil fuel use, such as social, environmental, and economic challenges. The political bioeconomy in the United Kingdom (UK) has supported a small but significant role for biomass within the country’s energy mix, with policy determining who benefits, and who will continue to benefit, from its use. The revised UK Biomass Strategy of 2023 signalled how the government perceives biomass looking forward, and the commitment to a cross-sectoral sustainability framework has the potential to support a redistributive policy that creates new winners in the UK biomass sector. Maximising the redistributive effects of policy is hindered by the siloed nature of policymaking around biomass and undermined by a lack of social legitimacy, both of which must be addressed to enable biomass to contribute towards decoupling the UK’s economy from fossil fuels and to ensure a sustainable transition. Full article
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<p>Interactions between policy, industry, and society, governed by legislation, markets, and a social contract, contextualising the non-technical factors impacting policymaking.</p>
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<p>A matrix of criteria in different sector policies applicable to biomass. Darker shading indicates that the criteria are accounted for, lighter shading indicates that the criteria are considered but not directly accounted for, and the lightest shading indicates that the criteria are not applicable within the policy. Adapted from Cucuzzella et al. (2020) [<a href="#B3-agronomy-14-02350" class="html-bibr">3</a>].</p>
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<p>Situating biomass sustainability trade-offs from Welfle et al. (2023) [<a href="#B5-agronomy-14-02350" class="html-bibr">5</a>] within the interactions between policy, industry, and society, in the context of how the relationships are governed by markets, legislation, and a social contract.</p>
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22 pages, 1363 KiB  
Article
Challenges in the Valorization of Green Waste in the Central European Region: Case Study of Warsaw
by Krystyna Lelicińska-Serafin, Anna Rolewicz-Kalińska and Piotr Manczarski
Energies 2024, 17(20), 5056; https://doi.org/10.3390/en17205056 - 11 Oct 2024
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Abstract
Expanding green areas in cities results in growth in green waste generation. This study presents the findings of an investigation into green waste from selective collection in a large Central European city (Warsaw, Poland), which can be identified as a valuable biomass resource. [...] Read more.
Expanding green areas in cities results in growth in green waste generation. This study presents the findings of an investigation into green waste from selective collection in a large Central European city (Warsaw, Poland), which can be identified as a valuable biomass resource. The research objective was to identify the properties of garden waste from single-family housing to determine valorization opportunities, emphasizing the utilization of GW as a source of energy. The research yielded several findings, including a notable degree of variability in fuel properties, including moisture content (CV = 30%), lower heating value (CV = 14.3%), and ash content (CV = 62.7/56.2%). The moisture content suggests composting, while the fertilizing properties indicate suitability for anaerobic digestion. The instability of the fuel properties, coupled with the elevated levels of chlorine, sulfur, and moisture, constrains the use of garden waste in thermal processes and alternative fuel production. Pyrolysis could be a viable approach for green waste feedstock, offering value-added products depending on the processing conditions and pre-treatment. Nevertheless, implementing a selective collection system is a critical condition for the optimal utilization of bio-waste, facilitating the quality and property control of green and food waste. This is essential for their effective processing, including energy recovery, thereby contributing to the efficient valorization of biomass. Full article
(This article belongs to the Special Issue Environmental Applications of Bioenergy and Biomass, 2nd Edition)
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<p>Photos of the tested GW fractions: (<b>a</b>) leaves, (<b>b</b>) grass, (<b>c</b>) cones, (<b>d</b>) branches.</p>
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<p>Mean values of the VS (%), MC (%), C/N, and C/P of four GW fractions collected selectively.</p>
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28 pages, 4845 KiB  
Review
From Potential to Power: Advancing Nigeria’s Energy Sector through Renewable Integration and Policy Reform
by Mohammad Awwal Adeshina, Abdulazeez M. Ogunleye, Habeeb Olaitan Suleiman, Abdulfatai Olatunji Yakub, Noel Ngando Same, Zainab Adedamola Suleiman and Jeung-Soo Huh
Sustainability 2024, 16(20), 8803; https://doi.org/10.3390/su16208803 - 11 Oct 2024
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Abstract
Nigeria is a nation endowed with both abundant renewable and non-renewable energy resources. Despite its vast potential, Nigeria struggles with a consistent power supply due to various systemic issues, such as inadequate funding, infrastructural decay, corruption, technical skill shortages, and macroeconomic instability. These [...] Read more.
Nigeria is a nation endowed with both abundant renewable and non-renewable energy resources. Despite its vast potential, Nigeria struggles with a consistent power supply due to various systemic issues, such as inadequate funding, infrastructural decay, corruption, technical skill shortages, and macroeconomic instability. These challenges hinder the effective harnessing and distribution of energy resources, particularly renewable ones like wind, solar, biomass, and hydropower. This study assesses the existing energy policies and their efficacy in promoting sustainable energy development towards achieving universal electricity access by 2030. It highlights the necessity for a just energy transition that integrates a substantial proportion of renewable energy into the national grid, aiming to meet up to 60% of the country’s energy demands with clean sources by 2050. This transition is critical not only for energy security and reducing the environmental impact but also for fostering socioeconomic equity. Recommendations include overhauling the legal and regulatory frameworks to support renewable energy growth, particularly in off-grid areas, to ensure clean, affordable, and secure energy access. Strategic investments, enhanced infrastructure, and robust public–private partnerships are essential to overcome the current barriers and realize Nigeria’s energy potential. This paper calls for a comprehensive approach that addresses both the technical and socioeconomic dimensions of the energy crisis, laying the groundwork for a sustainable and prosperous energy future for Nigeria. Full article
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<p>Sources of energy generation in Nigeria [<a href="#B19-sustainability-16-08803" class="html-bibr">19</a>].</p>
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<p>Biomass conversion technologies [<a href="#B28-sustainability-16-08803" class="html-bibr">28</a>].</p>
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<p>Contributions of biomass sources to the overall technical potential [<a href="#B15-sustainability-16-08803" class="html-bibr">15</a>].</p>
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<p>Global trend in installed wind energy capacity [<a href="#B41-sustainability-16-08803" class="html-bibr">41</a>].</p>
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<p>Wind speed distribution in Nigeria [<a href="#B43-sustainability-16-08803" class="html-bibr">43</a>].</p>
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<p>Comparison of the total installed and untapped global hydropower by region.</p>
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<p>Comparison of Nigeria’s hydropower: identified, total installed, and untapped capacities identified for both large and small hydropower.</p>
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<p>Illustration of geographical location and solar radiation distribution [<a href="#B106-sustainability-16-08803" class="html-bibr">106</a>].</p>
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<p>Implementation strategy for renewable energy in Nigeria.</p>
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14 pages, 3669 KiB  
Article
Manganese Oxide-Doped Hierarchical Porous Carbon Derived from Tea Leaf Waste for High-Performance Supercapacitors
by Hsiu-Ying Chung, Hong-Min Chang and Chun-Pang Wang
Int. J. Mol. Sci. 2024, 25(20), 10884; https://doi.org/10.3390/ijms252010884 - 10 Oct 2024
Viewed by 338
Abstract
Hierarchical porous carbon derived from discarded biomass for energy storage materials has attracted increasing research attention due to its cost-effectiveness, ease of fabrication, environmental protection, and sustainability. Brewed tea leaves are rich in heteroatoms that are beneficial to capacitive energy storage behavior. Therefore, [...] Read more.
Hierarchical porous carbon derived from discarded biomass for energy storage materials has attracted increasing research attention due to its cost-effectiveness, ease of fabrication, environmental protection, and sustainability. Brewed tea leaves are rich in heteroatoms that are beneficial to capacitive energy storage behavior. Therefore, we synthesized high electrochemical performance carbon-based composites from Tie guan yin tea leaf waste using a facile procedure comprising hydrothermal, chemical activation, and calcination processes. In particular, potassium permanganate (KMnO4) was incorporated into the potassium hydroxide (KOH) activation agent; therefore, during the activation process, KOH continued to erode the biomass precursor, producing abundant pores, and KMnO4 synchronously underwent a redox reaction to form MnO nanoparticles and anchor on the porous carbon through chemical bonding. MnO nanoparticles provided additional pseudocapacitive charge storage capabilities through redox reactions. The results show that the amount of MnO produced is proportional to the amount of KMnO4 incorporated. However, the specific surface area of the composite material decreases with the incorporated amount of KMnO4 due to the accumulation and aggregation of MnO nanoparticles, thereby even blocking some micropores. Optimization of MnO nanocrystal loading can promote the crystallinity and graphitization degree of carbonaceous materials. The specimen prepared with a weight ratio of KMnO4 to hydrochar of 0.02 exhibited a high capacitance of 337 F/g, an increase of 70%, owing to the synergistic effect between the Tie guan yin tea leaf-derived activated carbon and MnO nanoparticles. With this facile preparation method and the resulting high electrochemical performance, the development of manganese oxide/carbon composites derived from tea leaf biomass is expected to become a promising candidate as an energy storage material for supercapacitors. Full article
(This article belongs to the Special Issue Recent Advances in Electrochemical-Related Materials)
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<p>Schematic of the experimental process.</p>
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<p>XRD patterns of (<b>a</b>) TGC0, (<b>b</b>) TGC1, (<b>c</b>) TGC2, (<b>d</b>) TGC3, and (<b>e</b>) TGC4.</p>
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<p>Raman spectra of TGC specimens.</p>
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<p>SEM images of (<b>a</b>) TGC0, (<b>b</b>) TGC1, (<b>c</b>) TGC2, (<b>d</b>) TGC3, and (<b>e</b>) TGC4.</p>
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<p>(<b>a</b>) N<sub>2</sub> adsorption–desorption isotherms and (<b>b</b>) pore size distribution of TGC specimens.</p>
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<p>(<b>a</b>) Wide-scan XPS spectra of TGC specimens. High-resolution XPS spectra of TGC2 for (<b>b</b>) Mn 2p, (<b>c</b>) C 1s, and (<b>d</b>) O 1s.</p>
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<p>(<b>a</b>) GCD curves of TGC electrodes at a current density of 0.5 A/g. (<b>b</b>) GCD curves at current densities of 0.5, 1, 2, and 5 A/g, (<b>c</b>) CV curves at scan rates of 5, 10, 20, and 50 mV/s, and (<b>d</b>) EIS Nyquist plot of the TGC2 electrode.</p>
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