scholarly journals Artificial intelligence based modelling and multi-objective optimization of vinyl chloride monomer (VCM) plant to strike a balance between profit, energy utilization and environmental degradation

2022 ◽  
Vol 99 (1) ◽  
pp. 100287
Author(s):  
Sucharita Pal ◽  
Somnath Chowdhury ◽  
Abhiram Hens ◽  
Sandip Kumar Lahiri
Keyword(s):  
Artificial Intelligence ◽  
Vinyl Chloride ◽  
Environmental Degradation ◽  
Energy Utilization ◽  
Vinyl Chloride Monomer ◽  
Multi Objective Optimization ◽  
Multi Objective

scholarly journals Multi-Objective Optimization and Dispatch of Distributed Energy Resources for Renewable Power Utilization Considering Time-of-Use Tariff

2021 ◽  
Vol 9 ◽  
Author(s):  
Qinhao Xing ◽  
Meng Cheng ◽  
Shuran Liu ◽  
Qianliang Xiang ◽  
Hailian Xie ◽  
...  
Keyword(s):  
Energy Storage ◽  
Electric Vehicles ◽  
Energy Resources ◽  
Energy Utilization ◽  
Distributed Energy Resources ◽  
Multi Objective Optimization ◽  
Distributed Energy ◽  
Demand Forecast ◽  
Multi Objective ◽  
The Impact

The intermittency of wind and solar power generation brings risks to the safety and stability of the power system. In order to maximize the utilization of renewables, optimal control and dispatch methods of the Distributed Energy Resources including the generators, energy storage and flexible demand are necessary to be researched. This paper proposes an optimization and dispatch model of an aggregation of Distributed Energy Resources in order to facilitate the integration of renewables while considering the benefits for dispatchable resources under time-of-use tariff. The model achieves multi-objective optimization based on the constraints of day-ahead demand forecast, wind and solar generation forecast, electric vehicles charging routines, energy storage and DC power flow. The operating cost, the renewable energy utilization and the revenues of storages and electric vehicles are considered and optimized simultaneously through the min–max unification method to achieve the multi-objective optimization. The proposed model was then applied to a modified IEEE-30 bus case, demonstrating that the model is able to reconcile all participants in the system. Sensitivity analysis was undertaken to study the impact of initial states of the storages on the revenues to the resource owners.

scholarly journals Multi-Objective Optimization of Manufacturing Process in Carbon Fiber Industry Using Artificial Intelligence Techniques

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 67576-67588 ◽  
Author(s):  
Gelayol Golkarnarenji ◽  
Minoo Naebe ◽  
Khashayar Badii ◽  
Abbas S. Milani ◽  
Ali Jamali ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Carbon Fiber ◽  
Manufacturing Process ◽  
Multi Objective Optimization ◽  
Artificial Intelligence Techniques ◽  
Multi Objective

scholarly journals Multi-objective optimization of multi-microgrid power dispatch under uncertainties using interval optimization

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Shungen Luo ◽  
Xiuping Guo
Keyword(s):  
Optimization Problem ◽  
Operating Cost ◽  
Optimal Solution ◽  
Renewable Energies ◽  
Energy Utilization ◽  
Pollutant Emission ◽  
Interval Optimization ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Optimal Solution Set

<p style='text-indent:20px;'>The microgrid technology, which can dispatch power independently, is an effective way to increase the efficiency of energy utilization meanwhile develop and utilize the clean and renewable energy. However, the power generation of a single microgrid is unstable, because it is greatly affected by the external environment. Therefore, the development and application of the multi-microgrid system have gradually drawn various countries' attention. In order to minimize the operating cost and gaseous pollutant emission of the multi-microgrid system, which is composed of renewable energies and electric vehicles and so on, this paper builds a 24 hours day-ahead multi-objective complex constrained optimization model, using interval optimization to handle uncertainties of renewable energies. In view of the model characteristics, the metaheuristic strategies about initialization and repair of solution are designed. Furthermore, the fuzzy membership degree and Chebyshev function are used in parallel to decompose the multi-objective optimization problem, thus a multi-objective evolutionary algorithm based on hybrid decomposition (MOEA/HD) is constructed. Finally, the effectiveness of the metaheuristic strategies can be verified by analyzing the simulation results in this paper. Moreover, the results prove that the MOEA/HD is more efficient, which can get a higher-quality Pareto optimal solution set when compared to other algorithms.</p>

Artificial intelligence-based modelling and multi-objective optimization of friction stir welding of dissimilar AA5083-O and AA6063-T6 aluminium alloys

2016 ◽  
Vol 232 (4) ◽  
pp. 333-342 ◽  
Author(s):  
Saurabh Kumar Gupta ◽  
KN Pandey ◽  
Rajneesh Kumar
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Grain Size ◽  
Tensile Strength ◽  
Aluminium Alloys ◽  
Multi Objective Optimization ◽  
Friction Stir ◽  
Multi Objective ◽  
Artificial Neural

The present research investigates the application of artificial intelligence tool for modelling and multi-objective optimization of friction stir welding parameters of dissimilar AA5083-O–AA6063-T6 aluminium alloys. The experiments have been conducted according to a well-designed L27 orthogonal array. The experimental results obtained from L27 experiments were used for developing artificial neural network-based mathematical models for tensile strength, microhardness and grain size. A hybrid approach consisting of artificial neural network and genetic algorithm has been used for multi-objective optimization. The developed artificial neural network-based models for tensile strength, microhardness and grain size have been found adequate and reliable with average percentage prediction errors of 0.053714, 0.182092 and 0.006283%, respectively. The confirmation results at optimum parameters showed considerable improvement in the performance of each response.

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