Optimization of Flow Rate Distribution in a Crude Oil Preheat Train Considering Fouling Deposition in Shell and Tube Sides

Abstract

The deposition of an undesired material on the heat transfer surfaces is a recurrent problem that affects the overall performance of heat exchanger networks (HENs). A wide variety of empirical and semiempirical models are available for predicting the fouling behavior. In this work, there are considered different predominant fouling mechanisms at each side of the shell and tube heat exchangers used to preheat crude oil from a refinery. Adopting the mass flow rates at the parallel branches of the HEN as optimization variables, an optimization model is proposed to find the minimum total annual cost for 12 months of uninterrupted operation. The model was coded in MATLAB and solved using a simulated annealing algorithm. The results showed that fouling accumulation in both heat exchanger sides significantly impacted the heat recovery and increased operating costs. The proposed methodology was able to decrease the total annual cost in $4.6516 × 10 4 when compared to the base case.

A Deep Learning Hybrid Framework Combining an Efficient Evolutionary Algorithm for Complex Many-Objective Optimization of Sustainable Triple CO2 Feed Methanol Production

“…It exhibits nonconvex and nonlinear characteristics, thereby posing challenges in obtaining the entire set of Pareto optimal solutions through conventional optimization algorithms. Numerous researchers have proposed various algorithms, such as sequential quadratic programming, simulated annealing, and genetic algorithm. − Numerous studies have demonstrated that the utilization of NSGA-III yields remarkable results when addressing complex many-objective optimization issues. NSGA-III is a genetic algorithm employed for solving issues of multi-objective optimization.…”