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基于完全直觉模糊优化方法的纺织业可持续能源消耗模型。

Sustainable Energy Consumption Model for Textile Industry Using Fully Intuitionistic Fuzzy Optimization Approach.

机构信息

Department of Mathematics and Statistics, International Islamic University Islamabad, Islamabad, Pakistan.

Department of Mathematics, Faculty of Arts and Sciences Yildiz Technical University, Esenler 34210, Istanbul, Turkey.

出版信息

Comput Intell Neurosci. 2022 Aug 17;2022:5724825. doi: 10.1155/2022/5724825. eCollection 2022.

DOI:10.1155/2022/5724825
PMID:36035843
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9402327/
Abstract

Consumption of renewable energy is on the rise because new technologies have made it cheaper and easier to meet the needs of a long-term energy source. In the present study, the idea of optimal usage of sustainable energy is discussed, taking into consideration the environmental and economic conditions that exist in Pakistan's textile manufacturing industry. By taking into account the regional potential for the application of renewable energy resources, solar energy generators are taken into consideration, and a fully intuitionistic fuzzy (FIF) textile energy model is constructed. Using the FIF model to determine the optimal distribution of solar energy units resulted in a tolerable number of unused energy units. These units may be returned to the central power supply station, which would save both money and energy.

摘要

可再生能源的消费正在上升,因为新技术使得满足长期能源需求变得更加便宜和容易。在本研究中,考虑到巴基斯坦纺织制造业存在的环境和经济条件,讨论了可持续能源的最佳利用理念。考虑到可再生能源资源在该地区的应用潜力,考虑了太阳能发电机,并构建了一个完全直觉模糊(FIF)纺织能源模型。使用 FIF 模型来确定太阳能单元的最佳分布,产生了可接受数量的未使用能源单元。这些单元可以返回中央发电站,从而节省资金和能源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/b0bdb6bbbfff/CIN2022-5724825.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/3a71c26acdef/CIN2022-5724825.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/4324d4469ea3/CIN2022-5724825.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/e16feb8d36bb/CIN2022-5724825.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/e3f2a8b0dced/CIN2022-5724825.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/b0bdb6bbbfff/CIN2022-5724825.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/3a71c26acdef/CIN2022-5724825.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/4324d4469ea3/CIN2022-5724825.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/e16feb8d36bb/CIN2022-5724825.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/e3f2a8b0dced/CIN2022-5724825.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/07a8/9402327/b0bdb6bbbfff/CIN2022-5724825.005.jpg

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