Shivom Sharma

An improved multi-objective differential evolution with a termination criterion for optimizing chemical processes

Abstract Application problems have conflicting objectives and constraints, and maximum number of generations is the most common termination criterion in evolutionary algorithms used for solving these applications. This study develops a termination criterion using the non-dominated solutions obtained as the search progresses. For this, several performance metrics are modified, and their variation with generations has been assessed on many test functions. Based on this analysis, it is proposed to terminate the search if the improvement in variance of two selected performance metrics obtained in recent generations is statistically insignificant. Additionally, evaluation of objectives and constraints is computationally expensive in many applications. This study uses taboo list with multi-objective differential evolution to avoid re-visits and for better exploration of search space. Benefits of the termination criterion and taboo list are assessed on constrained benchmark problems. The developed approach is then evaluated on three chemical engineering applications, namely, alkylation, Williams-Otto and fermentation processes.

Development and Multiobjective Optimization of Improved Cumene Production Processes

Multiobjective optimization (MOO) has been successfully used to improve the process design and operation, by finding trade-offs among conflicting objectives such as energy, capital cost, and profit. In this work, the cumene process design is modified to decrease the raw materials and product losses and to facilitate better energy integration. Here, two slightly different cumene process designs are presented and evaluated. One process design uses a column to vent off the undesired chemicals, whereas the other uses two flash tanks. Additionally, vapor recompression is applied in both the designs to recover energy. Then, MOO of both modified process designs is carried out to examine two trade-offs: total capital cost (TCC) versus material loss and TCC versus utility cost. For this, an Excel-based MOO program is used; it is based on the elitist nondominated sorting genetic algorithm. The cumene process design with column is found to be superior for the first trade-off, whereas the design with two flash tanks is better for the other trade-off. Further, both the designs are compared based on their cumene production capacities; column design is found to be overall superior. Finally, energy requirements of the developed cumene process designs are compared with those reported in recent studies.

Economic and environmental criteria and trade-Offs for recovery processes

Volatile organic component (VOC) and solvent recovery processes are two industrially important processes to limit the release of potentially harmful components into the environment. The extent of environmental contamination depends on the recovery system used for the harmful components, which indirectly contributes to the cost of process. Recently, VOC and solvent recovery systems have been optimized for multiple economic and environmental criteria such as profit before tax (PBT), net present worth (NPW), and potential environmental impact (PEI). Besides PEI, there are other aggregate environmental indicators such as IMPact Assessment of Chemical Toxics 2002+ (IMPACT), green degree (GD), and inherent environmental toxicity hazard (IETH). In this study, we consider the optimization of VOC and solvent recovery processes for PBT, NPW, PEI, IETH, GD, and IMPACT as well as their individual components as simultaneous objectives, to explore the trade-offs among them. The two processes are simulated using a commercial simulator, and then optimized using the elitist nondominated sorting genetic algorithm in a spreadsheet along with an interface to the simulator. From the results, it can be concluded that optimization of aggregated IMPACT indicator is similar to optimization of its individual end-point damage categories. In the case of other environmental indicators, optimization for individual categories may be necessary to explore trade-offs among them.

Multi-objective Optimization of Heat Integrated Water Networks in Petroleum Refineries

Abstract Heat and water integrations for large scale chemical processes have become important due to economic and environmental reasons. These reduce consumption of both fresh water and energy, thus improving the sustainability of industrial processes. In chemical and related industries, water is used as a reactant, separation solvent and heating/cooling medium. An optimal water network reduces the consumption of fresh water by efficient reuse and recycling of water in the plant itself. In this work, onepetroleum refinery water network is optimized for two objectives: quantity of fresh water and total flow at the inlet of regenerator units simultaneously, using the e-constraint method. Then, selected optimal water network designs are studied for heat integration, using Aspen Energy Analyzer. The proposed approach gives better insights by providing a range of alternative designs, which is useful in the final selection of one optimal network design.

Multi-Objective Optimization Programs and their Application to Amine Absorption Process Design for Natural Gas Sweetening

This chapter presents three MS Excel programs, namely, EMOO (Excel based Multi-Objective Optimization), NDS (Non-Dominated Sorting) and PM (Performance Metrics) useful for Multi-Objective Optimization (MOO) studies. The EMOO program is for finding non-dominated solutions of a given MOO problem. It has both binary-coded and realcoded NSGA-II (Elitist Non-Dominated Sorting Genetic Algorithm), and two termination criteria based on chi-squared test and steady state detection. The known/true Pareto-optimal front for the application problems is not available unlike that for benchmark problems. Hence, a procedure for obtaining known/true Pareto-optimal front is described in this chapter. The NDS program is for non-dominated sorting and crowding distance calculations of the non-dominated solutions obtained from several optimization runs using same or different MOO programs. The PM program can be used to calculate the values of performance metrics between the non-dominated solutions obtained using a MOO program and the true/known Pareto optimal front. It is useful for comparing the performance of MOO programs to find the non-dominated solutions. Finally, use of EMOO, NDS and PM programs is demonstrated on MOO of amine absorption process for natural gas sweetening.

Multi-objective Optimization of a Membrane Distillation System for Desalination of Sea Water

Abstract Water scarcity around the world has led to drinking water production from sea and brackish water. Production of drinking water by membrane processes is capital and energy efficient compared to other processes. Membrane distillation (MD) is a thermally driven process, where low-grade waste heat or renewable energy can be used to produce drinking water. The performance of MD module, although depends on the membrane transport properties, can be improved by better module and process design. In this study, design of a MD module and process is optimized for both high water production rate and lower energy consumption simultaneously. Multi-objective optimization is performed to explore the trade-off between these conflicting performance criteria. The obtained results provide optimal designs of the MD process for different water production rate and energy consumption.

Jumping Gene Adaptations of NSGA-II with Altruism Approach: Performance Comparison and Application to Williams–Otto Process

Elitist non-dominated sorting genetic algorithm (NSGA-II) has been widely used for solving many application problems with multiple objectives. The concept of jumping gene (JG) from natural genetics has been incorporated into NSGA-II to improve its performance. Several JG adaptations have been proposed and used to solve multi-objective optimization test/application problems; aJG, saJG and sJG are recent JG adaptations, and they have similar performance. Further, the concept of altruism, inspired by the honey bee colony, has been incorporated with NSGA-II-aJG, and it has improved the search performance. In the present work, Alt-NSGA-II-aJG is modified for using saJG and sJG adaptations, and then performances of Alt-NSGA-II-aJG, Alt-NSGA-II-saJG and Alt-NSGA-II-sJG algorithms are compared on test and application problems. In the literature, the maximum number of generations (MNG) is the commonly used termination criterion for stochastic search algorithms. Hence, a search termination criterion based on the improvement in the Pareto-optimal front obtained has been included in the present study. Performance of selected algorithms is compared using both improvement-based termination criterion and MNG; here, generational distance, spread and inverse generational distance are employed to assess the quality of non-dominated solutions obtained. Results show that performance of Alt-NSGA-II-aJG, Alt-NSGA-II-saJG and Alt-NSGA-II-sJG algorithms is comparable, and use of the altruism approach and improvement-based termination criterion enhances the search algorithm significantly.

Multi-objective Optimization of a Fermentation Process Integrated with Cell Recycling and Inter-stage Extraction

Abstract Bio-fuels are clean, renewable energy with potential to replace fossil fuels. Bio-ethanol is the widely used bio-fuel, which can be produced from a variety of agricultural feedstocks. Its production from fermentable sugars is well established. Production of bio-ethanol from starchy and cellulosic materials requires hydrolysis as an additional step to produce fermentable sugars. In SHF (separate hydrolysis and fermentation) process, hydrolysis and fermentation are performed at their respective optimal temperatures, but end products (i.e., glucose and cellobiose) inhibit hydrolysis. SSF (simultaneous saccharification and fermentation) process removes product inhibition by immediate consumption of end products of hydrolysis. Ethanol concentration also inhibits glucose to ethanol conversion in the fermentor, which results in low ethanol productivity and yield. To avoid this, ethanol can be continuously removed from the fermentor using either extraction or perm-selective membrane. In this study, a three-stage fermentation process integrated with cell recycling and inter-stage extraction is considered, for producing ethanol from the lignocellulosic feed-stocks. The integrated process is optimized using a multi-objective differential evolution algorithm for two objectives simultaneously. Finally, improvement in the performance of the fermentation process due to inter-stage extraction is evaluated quantitatively.

Robust Multi-Objective Optimization of Solid Oxide Fuel Cell–Gas Turbine Hybrid Cycle and Uncertainty Analysis

Chemical process optimization problems often have multiple and conflicting objectives, such as capital cost, operating cost, production cost, profit, energy consumptions and environmental impacts. In such cases, Multi-Objective Optimization (MOO) is suitable in finding many Pareto optimal solutions, to understand the quantitative trade-offs among the objectives, and also to obtain the optimal values of decision variables. Gaseous fuel can be converted into heat, power and electricity, using combustion engine, gas turbine (GT) or Solid Oxide Fuel Cell (SOFC). Of these, SOFC with GT has shown higher thermodynamic performance. This hybrid conversion system leads to a better utilization of natural resource, reduced environmental impacts, and more profit. This study optimizes performance of SOFC-GT system for maximization of annual profit and minimization of annualized capital cost, simultaneously. For optimal SOFC-GT designs, the composite curves for maximum amount of possible heat recovery indicate good performance of the hybrid system. Further, first law energy and exergy efficiencies of optimal SOFC-GT designs are significantly better compared to traditional conversion systems. In order to obtain flexible design in the presence of uncertain parameters, robust MOO of SOFC-GT system was also performed. Finally, Pareto solutions obtained via 1 Corresponding author: shivom.sharma@epfl.ch 2 normal and robust MOO approaches are considered for parametric uncertainty analysis with respect to market and operating conditions, and solution obtained via robust MOO found to be less sensitive. INTRODUCTION Industrial process and energy system optimization problems have several conflicting objectives related to economics, energy, environment and safety [1]. MultiObjective Optimization (MOO) is useful in finding many optimal solutions, to understand the quantitative trade-offs among the objectives, and also to obtain the optimal values of different decision variables. There are several conversion technologies that can convert gaseous fuels (e.g., H2 and CH4) into heat, power and electricity. Natural Gas (NG) can be used as fuel in internal combustion engines, Gas Turbines (GT) or Solid Oxide Fuel Cells (SOFC). SOFC with GT has high thermodynamic performance that leads to a better utilization of natural resource, improved sustainability, and more profit [2]. The unconverted fuel from the fuel cell stack is combusted in a catalytic burner, and then it is used in a GT to produce additional electricity. Bio-syngas and bio-gas have significant amount of CO2, and they can directly be used as fuel in the SOFC-GT system without CO2 separation. Hence, SOFC-GT system also helps in the separation of CO2, which can be stored/used for different applications. Gasification converts biomass resource into bio-syngas, which has CH4, H2 and CO2 as main components. SOFC is a modern conversion technology, which has possibility of cogeneration, using different types of liquid and gaseous fuels. It can directly use biosyngas without the separation of CO2, or it can use bio-SNG (i.e., bio-syngas after CO2 separation). This study considers bio-gas as a fuel to the SOFC-GT system, which has 0.62