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The purpose of this study is to provide a method for designing the shell and tube heat exchangers and examine the total annual cost of heat exchanger networks from the economic view based on the careful design of equipment.

Accurate evaluation of heat exchanger networks performance depends on detailed models of heat exchangers design. The simulations variables include nine design variables such as flow direction determination of each of the two fluids, number of tubes, number of tube passes, length of tubes, the arrangement of tubes, size and percentage of baffle cut, tube diameter and tube pitch. The optimal designing of the heat exchangers is based on geometrical and hydraulic modeling and using a hybrid genetic particle swarm optimization algorithm (PSO-GA) technique. In this paper, optimization and minimization of the total annual cost of heat exchanger networks are considered as the objective function.

In this study, a fast and reliable method is used to simulate, optimize design parameters and evaluate heat transfer enhancement. PSO-GA algorithms have been used to minimize the total annual cost, which includes investment costs of heat exchangers and pumps, operating costs (pumping) and energy costs for utilities. Three case studies of four, six and nine streams are selected to demonstrate the accuracy of the method. Reductions of 0.55%, 23.5% and 14.78% are obtained in total annual cost for the selected streams, respectively.

In the present study, a reliable method is used to simulate and optimize design parameters and the economic optimization of the heat exchanger networks. Taking into account the importance of shell and tube heat exchangers in industrial applications and the complexity in their geometry, the PSO-GA methodology is adopted to obtain an optimal geometric configuration. The total annual cost is chosen as the objective function. Applying this technique to case studies demonstrates its ability to accurately design heat exchangers to optimize the objective function of the heat exchanger networks by giving the detail of design.

The purpose of this paper is to present a novel and applied method for optimum designing of plate-finned heat exchanger network. Considering the total annual cost as the objective function, a network of plate-finned heat exchanger is designed and optimized.

Accurate evaluation of plate-finned heat exchanger networks depends on different fin types with 10 different geometrical parameters of heat exchangers. In this study, fin numbers are considered as the main decision variables and geometrical parameters of fins are considered as the secondary decision variables. The algorithm applies heat transfer and pressure drop coefficients correction method and differential evolution (DE) algorithm to obtain the optimum results. In this paper, optimization and minimization of the total annual cost of heat exchanger network is considered as the objective function.

In this study, a novel and applied method for optimum designing of plate-finned heat exchanger network is presented. The comprehensive algorithm is applied into a case study and the results are obtained for both counter-flow and cross-flow plate-finned heat exchangers. The total annual cost and total area of the network with counter-flow heat exchangers were 12.5% and 23.27%, respectively, smaller than the corresponding values of the network with cross-flow heat exchanger.

In this paper, a reliable method is used to design, optimize parameters and the economic optimization of heat exchanger network. Taking into account the importance of plate-finned heat exchangers in industrial applications and the complexity in their geometry, the DE methodology is adopted to obtain an optimal geometric configuration. The total annual cost is chosen as the objective function. Applying this technique to a case study illustrates its capability to accurate design plate-finned heat exchangers to improve the objective function of the heat exchanger network from the economic viewpoint with the design of details.

The purpose of this study was to address waste management in the food supply chain (FSC) through the integration of inspection processes in production and distribution centers under uncertain conditions, aiming to enhance sustainability across environmental, economic and social dimensions. The study introduces a sustainable forward and reverse FSC network using a closed-loop supply chain network approach to prevent the transfer of spoiled products, ultimately providing competitive advantages to stakeholders.

A robust multi-objective mathematical programming model is proposed, incorporating inspection processes to manage perishable products effectively. The model is solved using the Augmented Epsilon Constraint technique implemented in GAMS software, providing Pareto-optimal solutions tailored to decision-makers’ preferences. Furthermore, the methodology is applied in a real-world case study and solved with the Benders Decomposition algorithm to validate its practicality and effectiveness.

The proposed methodology effectively minimizes waste and enhances sustainability in the FSC by optimizing decision-making processes under uncertainty. The illustrative examples and real case study demonstrate the efficiency of the model and solution approach, highlighting the significant role of inspection in improving all three dimensions of sustainability.

The study offers valuable insights into and tools for food industry managers to make informed strategic and tactical decisions. By addressing waste management through advanced supply chain modeling, the research helps organizations reduce costs, improve sustainability and gain a competitive edge in the market.

This research is novel in its focus on integrating inspection into the FSC network and addressing uncertainty through robust mathematical modeling. It contributes to the existing literature by demonstrating the impact of inspection on sustainability in FSCs and providing practical solutions for real-world implementation.