scholarly journals Modelling and Multi-Objective Optimization of the Sulphur Dioxide Oxidation Process

Processes ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 1072
Author(s):  
Mohammad Reza Zaker ◽  
Clémence Fauteux-Lefebvre ◽  
Jules Thibault
Keyword(s):  
Sulphuric Acid ◽  
Sulphur Dioxide ◽  
Variable Number ◽  
Inlet Temperature ◽  
Multi Objective Optimization ◽  
Absorption Column ◽  
Maximum Reaction Rate ◽  
Multi Objective ◽  
Maximum Reaction

Sulphuric acid (H2SO4) is one of the most produced chemicals in the world. The critical step of the sulphuric acid production is the oxidation of sulphur dioxide (SO2) to sulphur trioxide (SO3) which takes place in a multi catalytic bed reactor. In this study, a representative kinetic rate equation was rigorously selected to develop a mathematical model to perform the multi-objective optimization (MOO) of the reactor. The objectives of the MOO were the SO2 conversion, SO3 productivity, and catalyst weight, whereas the decisions variables were the inlet temperature and the length of each catalytic bed. MOO studies were performed for various design scenarios involving a variable number of catalytic beds and different reactor configurations. The MOO process was mainly comprised of two steps: (1) the determination of Pareto domain via the determination a large number of non-dominated solutions, and (2) the ranking of the Pareto-optimal solutions based on preferences of a decision maker. Results show that a reactor comprised of four catalytic beds with an intermediate absorption column provides higher SO2 conversion, marginally superior to four catalytic beds without an intermediate SO3 absorption column. Both scenarios are close to the ideal optimum, where the reactor temperature would be adjusted to always be at the maximum reaction rate. Results clearly highlight the compromise existing between conversion, productivity and catalyst weight.

A Thompson Sampling Efficient Multi-Objective Optimization Algorithm (TSEMO) for Lithium-Ion Battery Liquid-Cooled Thermal Management System: Study of Hydrodynamic, Thermodynamic, and Structural Performance

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
A. Garg ◽  
Cheng Liu ◽  
A. K. Jishnu ◽  
Liang Gao ◽  
My Loan Le Phung ◽  
...  
Keyword(s):  
Thermal Management ◽  
Optimization Algorithm ◽  
Cooling System ◽  
Hydrodynamic Performance ◽  
Design Parameters ◽  
Inlet Temperature ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Thompson Sampling ◽  
Conflicting Objectives

Abstract The efficient design of battery thermal management systems (BTMSs) plays an important role in enhancing the performance, life, and safety of electric vehicles (EVs). This paper aims at designing and optimizing cold plate-based liquid cooling BTMS. Pitch sizes of channels, inlet velocity, and inlet temperature of the outermost channel are considered as design parameters. Evaluating the influence and optimization of design parameters by repeated computational fluid dynamics calculations is time consuming. To tackle this, the effect of design parameters is studied by using surrogate modeling. Optimized design variables should ensure a perfect balance between certain conflicting goals, namely, cooling efficiency, BTMS power consumption (parasitic power), and size of the battery. Therefore, the optimization problem is decoupled into hydrodynamic performance, thermodynamic performance, and mechanical structure performance. The optimal design involving multiple conflicting objectives in BTMS is solved by adopting the Thompson sampling efficient multi-objective optimization algorithm. The results obtained are as follows. The optimized average battery temperature after optimization decreased from 319.86 K to 319.2759 K by 0.18%. The standard deviation of battery temperature decreased from 5.3347 K to 5.2618 K by 1.37%. The system pressure drop decreased from 7.3211 Pa to 3.3838 Pa by 53.78%. The performance of the optimized battery cooling system has been significantly improved.

Thermodynamic and Economic Analysis and Multi-Objective Optimization of Supercritical CO2 Brayton Cycles

2015 ◽  
Author(s):  
Hang Zhao ◽  
Qinghua Deng ◽  
Wenting Huang ◽  
Zhenping Feng
Keyword(s):  
Supercritical Co2 ◽  
Exergy Efficiency ◽  
Temperature Cycle ◽  
Inlet Temperature ◽  
Brayton Cycle ◽  
Multi Objective Optimization ◽  
Turbine Inlet Temperature ◽  
Multi Objective ◽  
Turbine Inlet ◽  
Thermodynamic Performance

Supercritical CO2 Brayton cycles (SCO2BC) offer the potential of better economy and higher practicability due to their high power conversion efficiency, moderate turbine inlet temperature, compact size as compared with some traditional working fluids cycles. In this paper, the SCO2BC including the SCO2 single-recuperated Brayton cycle (RBC) and recompression recuperated Brayton cycle (RRBC) are considered, and flexible thermodynamic and economic modeling methodologies are presented. The influences of the key cycle parameters on thermodynamic performance of SCO2BC are studied, and the comparative analyses on RBC and RRBC are conducted. Based on the thermodynamic and economic models and the given conditions, the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used for the Pareto-based multi-objective optimization of the RRBC, with the maximum exergy efficiency and the lowest cost per power ($/kW) as its objectives. In addition, the Artificial Neural Network (ANN) is chosen to establish the relationship between the input, output, and the key cycle parameters, which could accelerate the parameters query process. It is observed in the thermodynamic analysis process that the cycle parameters such as heat source temperature, turbine inlet temperature, cycle pressure ratio, and pinch temperature difference of heat exchangers have significant effects on the cycle exergy efficiency. And the exergy destruction of heat exchanger is the main reason why the exergy efficiency of RRBC is higher than that of RBC under the same cycle conditions. Compared with the two kinds of SCO2BC, RBC has a cost advantage from economic perspective, while RRBC has a much better thermodynamic performance, and could rectify the temperature pinching problem that exists in RBC. Therefore, RRBC is recommended in this paper. Furthermore, the Pareto front curve between the cycle cost/ cycle power (CWR) and the cycle exergy efficiency is obtained by multi-objective optimization, which indicates that there is a conflicting relation between them. The optimization results could provide an optimum trade-off curve enabling cycle designers to choose their desired combination between the efficiency and cost. Moreover, the optimum thermodynamic parameters of RRBC can be predicted with good accuracy using ANN, which could help the users to find the SCO2BC parameters fast and accurately.

A Study on Multi-Objective Optimization of Abrasive Blasting Systems

2010 ◽  
Vol 126-128 ◽  
pp. 29-34 ◽  
Author(s):  
Vu Ngoc Pi ◽  
Tran Minh Duc
Keyword(s):  
Boron Carbide ◽  
Optimization Problem ◽  
Nozzle Diameter ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Nozzle Wear ◽  
The Cost ◽  
Cost Components ◽  
Abrasive Blasting

This paper introduces a study on a multi-objective optimization problem of abrasive blasting systems. The aim of the study is to find the optimum exchanged diameter of boron carbide nozzles. In the study, the effects of several parameters such as the maximum nozzle diameter, the nozzle wear and the cost components on the optimum initial nozzle diameter were taken into account. From the study, a regression model for determination of the optimum initial diameter of boron carbide nozzles was introduced.

Thermodynamic and Economic Analysis and Multi-objective Optimization of Supercritical CO2 Brayton Cycles

2016 ◽  
Vol 138 (8) ◽  
Author(s):  
Hang Zhao ◽  
Qinghua Deng ◽  
Wenting Huang ◽  
Dian Wang ◽  
Zhenping Feng
Keyword(s):  
Supercritical Co2 ◽  
Pressure Ratio ◽  
Economic Modeling ◽  
Exergy Efficiency ◽  
Temperature Cycle ◽  
Inlet Temperature ◽  
Brayton Cycle ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Thermodynamic Performance

Supercritical CO2 Brayton cycles (SCO2BC) including the SCO2 single-recuperated Brayton cycle (RBC) and recompression recuperated Brayton cycle (RRBC) are considered, and flexible thermodynamic and economic modeling methodologies are presented. The influences of the key cycle parameters on thermodynamic performance of SCO2BC are studied, and the comparative analyses on RBC and RRBC are conducted. Nondominated Sorting Genetic Algorithm II (NSGA-II) is selected for the Pareto-based multi-objective optimization of the RRBC, with the maximum exergy efficiency and the lowest cost per power (k$/kW) as its objectives. Artificial neural network (ANN) is chosen to accelerate the parameters query process. It is shown that the cycle parameters such as heat source temperature, turbine inlet temperature, cycle pressure ratio, and pinch temperature difference of heat exchangers have significant effects on the cycle exergy efficiency. The exergy destruction of heat exchanger is the main reason why the exergy efficiency of RRBC is higher than that of the RBC under the same cycle conditions. RBC has a cost advantage from economic perspective, while RRBC has a much better thermodynamic performance, and could rectify the temperature pinching problem that exists in RBC. It is also shown that there is a conflicting relationship between the cycle cost/cycle power (CWR) and the cycle exergy efficiency. The optimization results could provide an optimum tradeoff curve enabling cycle designers to choose their desired combination between the efficiency and cost. ANN could help the users to find the SCO2BC parameters fast and accurately.

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