scholarly journals Quick Screening of Pareto-Optimal Operating Conditions for Expanding Solvent–Steam Assisted Gravity Drainage Using Hybrid Multi-Objective Optimization Approach

Energies ◽  
2017 ◽  
Vol 10 (7) ◽  
pp. 966 ◽  
Author(s):  
Baehyun Min ◽  
Krupa Kannan ◽  
Sanjay Srinivasan
Keyword(s):  
Operating Conditions ◽  
Gravity Drainage ◽  
Pareto Optimal ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Optimal Operating ◽  
Multi Objective ◽  
Steam Assisted Gravity Drainage

scholarly journals Integration of Operability Framework and Pareto Optimal Front to Calculate Optimal Condition of Ethane Cracking Process

2020 ◽  
pp. 105-113
Author(s):  
M. Farsi
Keyword(s):  
Pareto Front ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Base Case ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Cracking Process

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.  

Multi-objective optimization of an industrial slurry phase ethylene polymerization reactor

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Amit K. Thakur ◽  
Santosh K. Gupta ◽  
Rahul Kumar ◽  
Nilanjana Banerjee ◽  
Pranava Chaudhari
Keyword(s):  
Ethylene Polymerization ◽  
Operating Cost ◽  
Operating Conditions ◽  
Multi Objective Optimization ◽  
Reactor Model ◽  
Optimal Operating ◽  
Nsga Ii ◽  
Multi Objective ◽  
Maximum Productivity ◽  
Slurry Phase

Abstract Slurry polymerization processes using Zeigler–Natta catalysts are most widely used for the production of polyethylene due to their several advantages over other processes. Optimal operating conditions are required to obtain the maximum productivity of the polymer at minimal cost while ensuring operational safety in the slurry phase ethylene polymerization reactors. The main focus of this multi-objective optimization study is to obtain the optimal operating conditions corresponding to the maximization of productivity and yield at a minimal operating cost. The tuned reactor model has been optimized. The single objective optimization (SOO) and multi-objective optimization (MOO) problems are solved using non-dominating sorting genetic algorithm-II (NSGA-II). A complete range of Pareto optimal solutions are obtained to obtain the maximum productivity and polymer yield at different input costs.

Pareto-Based Multi-Objective Optimization of Recuperated S-CO2 Brayton Cycles

2014 ◽  
Author(s):  
Mahmood Mohagheghi ◽  
Jayanta Kapat ◽  
Narasimha Nagaiah
Keyword(s):  
System Optimization ◽  
Superior Performance ◽  
Specific Power ◽  
Pareto Optimal ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Conflicting Objectives ◽  
Cycle Efficiency ◽  
Pareto Optimal Fronts

In this paper, two configurations of the S-CO2 Brayton cycles (i.e., the single-recuperated and recompression cycles) are thermodynamically modeled and optimized through a multi-objective approach. Two semi-conflicting objectives, i.e., cycle efficiency (ηc) and cycle specific power (Φsp) are maximized simultaneously to achieve Pareto optimal fronts. The objective of maximum cycle efficiency is to have a smaller and less expensive solar field, and a lower fuel cost in case of a hybrid scheme. On the other hand, the objective of maximum specific power provides a smaller power block, and a lower capital cost associated with recuperators and coolers. The multi-objective optimization is carried out by means of a genetic algorithm which is a robust method for multidimensional, nonlinear system optimization. The optimization process is comprehensive, i.e., all the decision variables including the inlet temperatures and pressures of turbines and compressors, the pinch point temperature differences, and the mass flow fraction of the main compressor are optimized simultaneously. The presented Pareto optimal fronts provide two optimum trade-off curves enabling decision makers to choose their desired compromise between the objectives, and to avoid naive solution points obtained from a single-objective optimization approach. Moreover, the comparison of the Pareto optimal fronts associated with the studied configurations reveals the optimum operational region of the recompression configuration where it presents superior performance over the single-recuperated cycle.

Multi Objective Optimization of Direct Coupling Photovoltaic-Electrolyzer Systems Using Imperialist Competitive Algorithm

2014 ◽  
Author(s):  
Azadeh Maroufmashat ◽  
Farid Sayedin ◽  
Sourena Sattari
Keyword(s):  
Energy Transfer ◽  
Hydrogen Production ◽  
Minimum Energy ◽  
Imperialist Competitive Algorithm ◽  
Operating Conditions ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Competitive Algorithm ◽  
Pem Electrolyzer

Photovoltaic-electrolyzer systems are one of the most promising alternatives for obtaining hydrogen from a renewable energy source. Determining size and the operational conditions are always a key issue while coupling directly renewable electricity sources to PEM electrolyzer. In this research, the multi objective optimization approach based on an imperialist competitive algorithm (ICA), which is employed to optimize the size and the operating conditions of a directly coupled photovoltaic (PV)-PEM electrolyzer. This allows the optimization of the system by considering two different objectives, including, minimization of energy transfer loss and maximization of hydrogen generation. Multi objective optimization of PV/EL system predicts a maximum hydrogen production of 7930 gr/yr for energy transfer loss of 16.48 kWh/yr and minimum energy transfer loss of 5.21 kWh/yr at a hydrogen production rate of 7760 gr/yr for a the given location and the PV module.

Utilizing Multiple Objectives for the Optimization of the Pultrusion Process Based on a Thermo-Chemical Simulation

2013 ◽  
Vol 554-557 ◽  
pp. 2165-2174 ◽  
Author(s):  
Cem C. Tutum ◽  
Ismet Baran ◽  
Jesper Hattel
Keyword(s):  
Optimization Problem ◽  
Operating Conditions ◽  
Die Design ◽  
Process Efficiency ◽  
Optimum Process ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Pultrusion Process ◽  
Multi Objective

Pultrusion is one of the most effective manufacturing processes for producing composites with constant cross-sectional profiles. This obviously makes it more attractive for both researchers and practitioners to investigate the optimum process parameters, i.e. pulling speed, power and dimensions of the heating platens, length and width of the heating die, design of the resin injection chamber, etc., to provide better understanding of the process, consequently to improve the efficiency of the process as well the product quality. Numerous simulation approaches have been presented until now. However, optimization studies had been limited with either experimental cases or determining only one objective to improve one aspect of the performance of the process. This objective is either augmented by other process related criteria or subjected to constraints which might have had the same importance of being treated as objectives. In essence, these approaches convert a true multi-objective optimization problem (MOP) into a single-objective optimization problem (SOP). This transformation obviously results in only one optimum solution and it does not support the efforts to get more out of an optimization study, such as relations between variables and objectives or constraints. In this study, an MOP considering thermo-chemical aspects of the pultrusion process (e.g. cure degree, temperatures), in which the pulling speed is maximized and the heating power is minimized simultaneously (without defining any preference between them), has been formulated. An evolutionary multi-objective optimization (EMO) algorithm, non-dominated sorting genetic algorithm (NSGA-II [Deb et al., 2002]), has been used to solve this MOP in an ideal way where the outcome is the set of multiple solutions (i.e. Pareto-optimal solutions) and each solution is theoretically an optimal solution corresponding to a particular trade-off among objectives. Following the solution process, in other words obtaining the Pareto-optimal front, a further postprocessing study has been performed to unveil some common principles existing between the variables, the objectives and the constraints either along the whole front or in some portion of it. These relationships will reveal a design philosophy not only for the improvement of the process efficiency, but also a methodology to design a pultrusion die for different operating conditions.

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