Nadeem Khalfe

Particle swarm optimization technique for the optimal design of shell and tube heat exchangers

Abstract Owing to the wide utilization of heat exchangers in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters until given heat duty and set of geometric and operational constraints are satisfied.Although well proven, this kind of approach is time consuming and may not lead to cost effective design. The present study explores the use of non-traditional optimization technique: calledParticle swarm optimization (PSO), for design optimization of shell and tube heat exchangers from economic point of view. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tubelength, bafflespacing, number of tube passes, tubelayout, type of head, baffle cutetc and minimization of total annual cost is considered as design target. The presented PSO technique is conceptually simple, has only a few parameters and is easy to implement.Furthermore, the PSO algorithm explores the good quality solutions quickly, giving the designer more degrees of freedom in the final choice with respect to traditional methods. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm . The PSO method leads to a design of a heat exchanger with a reduced cost of heat exchanger as compare to cost obtained by previously reported GA approach.

Hybrid Particle Swarm Optimization and Ant Colony Optimization Technique for the Optimal Design of Shell and Tube Heat Exchangers

Abstract Owing to the wide utilization of shell and tube heat exchangers (STHEs) in industrial processes, their cost minimization is an important target for both designers and users. Traditional design approaches are based on iterative procedures which gradually change the design and geometric parameters until satisfying a given heat duty and set of geometric and operational constraints. Although well proven, this kind of approach is time-consuming and may not lead to cost-effective design. The present study explores the use of non-traditional optimization technique called hybrid particle swarm optimization (PSO) and ant colony optimization (ACO), for design optimization of STHEs from economic point of view. The PSO applies for global optimization and ant colony approach is employed to update positions of particles to attain rapidly the feasible solution space. ACO works as a local search, wherein ants apply pheromone-guided mechanism to update the positions found by the particles in the earlier stage. The optimization procedure involves the selection of the major geometric parameters such as tube diameters, tube length, baffle spacing, number of tube passes, tube layout, type of head, baffle cut, etc. and minimization of total annual cost is considered as design target. The methodology takes into account the geometric and operational constraints typically recommended by design codes. Three different case studies are presented to demonstrate the effectiveness and accuracy of proposed algorithm. The examples analyzed show that the hybrid PSO and ACO algorithm provides a valuable tool for optimal design of heat exchanger. The hybrid PSO and ACO approach is able to reduce the total cost of heat exchanger as compare to cost obtained by previously reported genetic algorithm (GA) approach. The result comparisons with particle swarm optimizer and other optimization algorithms (GA) demonstrate the effectiveness of the presented method.

Soft sensor for better control of carbon dioxide removal process in ethylene glycol plant

Low carbon dioxide in cycle gas loop of ethylene glycol (EG) plant improves catalyst selectivity and overall economics of the plant. Carbon dioxide produced as a by-product in ethylene oxide reactor is removed by the Benfield process. In this process, the carbonate and bicarbonate ratio in lean carbonate solution is considered as an important quality control (QC) variable as the efficiency of carbon dioxide removal largely depends on it. In the event of a process malfunction or operating under suboptimal condition, the CO2 content in the cycle gas loop will continue to rise until corrective action is taken after obtaining lab results for carbonate and bicarbonate ratio. This time consuming sampling process can be overcome by implementing a technological solution in form of an accurate and robust mathematical model capable of real time QC variable prediction. For well understood processes, the structure of the correlation for QC variables as well as the choice of the inputs may be well known in advance. However, the Benfield process is too complex and the appropriate form of the correlation and choice of input variables are not obvious. Here, knowledge of the processes, operating experience and statistical methods were applied in developing the soft sensor. This paper describes a systematic approach to the development of inferential measurements of carbonate and bicarbonate ratio using Support Vector Regression (SVR) analysis. Given historical process data, a simple SVR-based soft sensor model is found capable of identifying and capturing the cause and effect relationship between operating variables (model inputs) and QC variables (model outputs). Special care was taken to choose input variables, so that the final correlation and regression coefficient make senses from process engineering point of view. The developed soft sensor was implemented in commercial ethylene glycol plant in an Exaquantum interface and was found to satisfactorily predict the carbonate and bicarbonate ratio in real time.