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Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm

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Abstract

Heat exchangers play a key role in wide industrial applications. Due to their complex design and high manufacturing cost, their efficient operation and optimum design are quite important for overall cost minimization. There have been several optimization algorithms developed so far for the optimum design of the shell-and-tube heat exchanger (STHE). In this paper, the ability to emerge AI-based optimization method referred to as cohort intelligence (CI) is demonstrated by solving the design and economic optimization of the STHEs. Three cases were solved. These three cases include fluids, which are different at both the shell side and tube side with different inlet and outlet temperatures at the shell side and tube side. The associated key variables such as tube outside diameter, baffle spacing, pitch size, shell inside diameter and number of tube passes that decide the total cost of the heat exchanger were optimized. The performance of the CI method is compared with existing algorithms. The quality and robustness of the CI solution at reasonable computational cost highlighted its applicability by solving real-world problems from mechanical engineering domain.

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Abbreviations

b :

Baffle spacing (\({\text{m}}\))

C p :

Specific heat (\({\text{kJ}}/{\text{kg}}\,{\text{K}}\))

C inv :

Capital investment \((\EUR)\)

C E :

Energy cost (\(\EUR/{\text{kW}}\,{\text{h}}\))

C annual :

Annual operating cost \((\EUR/{\text{year}})\)

\(C_{{{\text{total}}\_{\text{disc}}}}\) :

Total discounted operating cost \((\EUR)\)

\(C_{\text{total}}\) :

Total annual cost \((\EUR)\)

\(d\) :

Tube diameter (m)

\(D\) :

Shell diameter (m)

\(f\) :

Friction factor

\(F\) :

Correction factor

\(h\) :

Heat transfer coefficient (\({\text{W}}/{\text{m}}^{2} {\text{K}}\))

\(A\) :

Annual operating time (\({\text{h}}/{\text{year}}\))

\(I\) :

Annual discount rate \(\left( \% \right)\)

\(k\) :

Thermal conductivity (\({\text{W}}/{\text{m K}}\))

\(L\) :

Tube length (m)

\(m\) :

Mass flow rate (\({\text{kg}}/{\text{s}}\))

\(n_{\text{t}}\) :

Number of tube passes

\(n_{\text{y}}\) :

Equipment life (year)

\(N_{\text{t}}\) :

Number of tubes

\(G\) :

Pumping power (W)

\(Pr\) :

Prandtl number

\(P_{\text{t}}\) :

Tube pitch (m)

\(Q\) :

Heat transfer rate (W)

\({Re}\) :

Reynolds number

\(R_{\text{l}}\) :

Fouling resistance (\({\text{m}}^{2} {\text{K/W}}\))

\(S\) :

Heat transfer surface area (m2)

\(T\) :

Temperature (K)

\(H\) :

Overall heat transfer coefficient (\({\text{W}}/{\text{m}}^{2} {\text{K}}\))

\(v\) :

Fluid velocity (m/s)

\(\Delta P\) :

Pressure drop (Pa)

\(\Delta T_{\text{LM}}\) :

Logarithmic mean temperature difference (°C)

\(\mu\) :

Dynamic viscosity (Pas)

\(\vartheta\) :

Kinematic viscosity (m2/s)

\(\rho\) :

Density (kg/m3)

\(\eta\) :

Overall pumping efficiency

i:

Inlet

o:

Outlet

s:

Belonging to shell

t:

Belonging to the tube

e:

Equivalent

w:

Tube wall

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Acknowledgements

The authors would like to thank the anonymous reviewers for their comments which certainly helped to enrich the quality of the work presented in the manuscript.

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Authors and Affiliations

  1. Symbiosis Institute of Technology, Symbiosis International University, Pune, MH, 412 115, India

    Shreesh V. Dhavle, Anand J. Kulkarni, Apoorva Shastri & Ishaan R. Kale

  2. Odette School of Business, University of Windsor, 401 Sunset Avenue, Windsor, ON, N9B3P4, Canada

    Anand J. Kulkarni

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Dhavle, S.V., Kulkarni, A.J., Shastri, A. et al. Design and economic optimization of shell-and-tube heat exchanger using cohort intelligence algorithm. Neural Comput & Applic 30, 111–125 (2018). https://doi.org/10.1007/s00521-016-2683-z

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