Zhang et al., 2020 - Google Patents
An integrated prediction model of heavy metal ion concentration for iron electrocoagulation processZhang et al., 2020
- Document ID
- 7092322476050080614
- Author
- Zhang F
- Yang C
- Zhu H
- Li Y
- Gui W
- Publication year
- Publication venue
- Chemical Engineering Journal
External Links
Snippet
In the electrocoagulation process of the heavy metal wastewater treatment, the acquisition of the heavy metal ions concentration at outlet requires long-term analysis, resulting in delayed control of the process and many other continuing problems. This study focuses on …
- 238000000034 method 0 title abstract description 75
Classifications
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/46—Treatment of water, waste water, or sewage by electrochemical methods
- C02F1/461—Treatment of water, waste water, or sewage by electrochemical methods by electrolysis
- C02F1/46104—Devices therefor; Their operating or servicing
-
- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2101/00—Nature of the contaminant
- C02F2101/10—Inorganic compounds
- C02F2101/20—Heavy metals or heavy metal compounds
- C02F2101/22—Chromium or chromium compounds, e.g. chromates
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Zhang et al. | An integrated prediction model of heavy metal ion concentration for iron electrocoagulation process | |
Wang et al. | A deep learning based dynamic COD prediction model for urban sewage | |
Aber et al. | Removal of Cr (VI) from polluted solutions by electrocoagulation: Modeling of experimental results using artificial neural network | |
Huang et al. | Multi‐objective optimisation for design and operation of anaerobic digestion using GA‐ANN and NSGA‐II | |
Nasr et al. | Artificial intelligence for greywater treatment using electrocoagulation process | |
Abbar et al. | Cadmium removal using a spiral-wound woven wire meshes packed bed rotating cylinder electrode | |
Asadi et al. | Greenhouse gas emission estimation from municipal wastewater using a hybrid approach of generative adversarial network and data-driven modelling | |
CN114690700B (en) | PLC-based intelligent sewage treatment decision optimization method and system | |
Bonakdari et al. | Calculating the energy consumption of electrocoagulation using a generalized structure group method of data handling integrated with a genetic algorithm and singular value decomposition | |
CN105975800B (en) | Multi-parameter optimizing method and device for chemical heavy metal waste water treatment process | |
Li et al. | Development of an adversarial transfer learning-based soft sensor in industrial systems | |
Gholikandi et al. | Performance prediction and upgrading of electroanaerobic baffled reactor using neural-fuzzy method | |
Uzoh et al. | Electrocoagulation of Pb2+, Co2+, and Mn2+ from simulated wastewater: an algorithmic optimization using hybrid RSM–GA–PSO | |
Bagheri et al. | Analysis of variables affecting mixed liquor volatile suspended solids and prediction of effluent quality parameters in a real wastewater treatment plant | |
An et al. | Adaptive prediction for effluent quality of wastewater treatment plant: Improvement with a dual-stage attention-based LSTM network | |
Behroozpour et al. | Prediction of the continuous cadmium removal efficiency from aqueous solution by the packed-bed column using GMDH and ANFIS models | |
Ou et al. | Sequential dynamic artificial neural network modeling of a full-scale coking wastewater treatment plant with fluidized bed reactors | |
Wan et al. | Deep learning-based intelligent management for sewage treatment plants | |
Chang | Soft measurement modeling of turbidity in flocculation process of drinking water treatment using gaussian process regression | |
Gholikandi et al. | Fered-Fenton technology for efficient waste-activated sludge stabilization: Determination of the main specifications and optimization of the energy consumption | |
CN116796905A (en) | A method for predicting water output indicators of microbial reactors during landfill leachate treatment | |
Huang et al. | Modeling and optimization of the activated sludge process | |
Ramesh et al. | Relevance of artificial intelligence in wastewater management | |
Huang et al. | Modeling of a paper-making wastewater treatment process using a fuzzy neural network | |
Long et al. | A mechanism-based semisupervised online pH estimation approach for a leaching process |