A multi-objective optimal collaborative gas flow regulation method for non-stationary ventilation network based on improved NSGA-Ⅲ algorithm

通风(建筑) 计算机科学 流量(数学) 数学优化 算法 工程类 数学 机械工程 几何学
作者
Kai Wang,Zibo Ai,Aitao Zhou,Qiang Fu,Wei Zhao
出处
期刊:Advanced Engineering Informatics [Elsevier]
卷期号:61: 102486-102486
标识
DOI:10.1016/j.aei.2024.102486
摘要

After the shockwave of a coal and gas outburst disappears, the influx of a high concentration of gas causes ventilation network disorder. This will further lead to the occurrence of secondary disasters, resulting in serious human and economic losses. This study focuses on solving the cooperative regulation problem of non-stationary ventilation networks (NSVNs) after a disaster. The NSVN solution model is dynamically combined with the ventilation network regulation method to simulate the results of post-disaster cooperative regulation. The Pareto optimal solution for decision variables is obtained with the goals of reducing the affected area, preventing the reversal of branch airflow, and rapidly discharging the high concentration of gas. In this regard, an NSGA-III algorithm based on segmented hybrid coding with conditional distribution sampling is designed to solve the problem of the population crossover operation under the dynamic change of the decision variables. In addition, according to the applicability of the decision variables in the regulation of NSVNs, the sampling distribution function that meets the practical application requirements is selected. Moreover, conditional distribution sampling is carried out for chromosome generation via real number coding, the aim of which is to improve the search efficiency and optimization effect. Via the hierarchical logical relationship between the decision variables, conversion is carried out to generate population individuals of different dimensions to realize the dynamic inputs of the NSVN solution model and calculate the corresponding target values. Finally, the feasibility of the proposed model and solution method is verified by studying and analyzing the case of an airflow disaster in a coal mine in Jiulishan, China. This study provides theoretical support for the cooperative regulation of NSVNs, as well as guidance for the post-disaster emergency rescue work in possible coal and gas herniation accidents.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
1秒前
英姑应助kk采纳,获得10
1秒前
maodou发布了新的文献求助30
1秒前
3秒前
李爱国应助小王采纳,获得30
4秒前
Imooinkk发布了新的文献求助10
6秒前
爆米花应助maodou采纳,获得10
6秒前
6秒前
8秒前
9秒前
称心的沛柔完成签到 ,获得积分10
10秒前
暴躁四叔应助月亮是甜的采纳,获得30
10秒前
科研小白发布了新的文献求助10
11秒前
JQK完成签到 ,获得积分20
11秒前
hmfyl发布了新的文献求助10
12秒前
脑洞疼应助雨碎寒江采纳,获得10
12秒前
13秒前
SAIL完成签到 ,获得积分10
13秒前
14秒前
JonyQ完成签到,获得积分20
16秒前
Stephen发布了新的文献求助10
17秒前
18秒前
鹅鹅鹅完成签到,获得积分10
19秒前
19秒前
小马甲应助禹代秋采纳,获得10
19秒前
19秒前
雨天发布了新的文献求助10
19秒前
小翁完成签到 ,获得积分10
19秒前
21秒前
hmfyl完成签到,获得积分10
22秒前
请叫我风吹麦浪应助cjh采纳,获得10
22秒前
一一应助cjh采纳,获得10
22秒前
Song发布了新的文献求助10
22秒前
小蘑菇应助玩命的凝天采纳,获得10
26秒前
Cloud发布了新的文献求助10
26秒前
你香发布了新的文献求助10
28秒前
dominate完成签到,获得积分10
29秒前
鹅鹅鹅发布了新的文献求助10
30秒前
Imooinkk完成签到,获得积分10
30秒前
31秒前
高分求助中
Востребованный временем 2500
Agaricales of New Zealand 1: Pluteaceae - Entolomataceae 1040
Healthcare Finance: Modern Financial Analysis for Accelerating Biomedical Innovation 1000
지식생태학: 생태학, 죽은 지식을 깨우다 600
海南省蛇咬伤流行病学特征与预后影响因素分析 500
Neuromuscular and Electrodiagnostic Medicine Board Review 500
ランス多機能化技術による溶鋼脱ガス処理の高効率化の研究 500
热门求助领域 (近24小时)
化学 医学 材料科学 生物 工程类 有机化学 生物化学 纳米技术 内科学 物理 化学工程 计算机科学 复合材料 基因 遗传学 物理化学 催化作用 细胞生物学 免疫学 电极
热门帖子
关注 科研通微信公众号,转发送积分 3463248
求助须知:如何正确求助?哪些是违规求助? 3056670
关于积分的说明 9053304
捐赠科研通 2746544
什么是DOI,文献DOI怎么找? 1507004
科研通“疑难数据库(出版商)”最低求助积分说明 696248
邀请新用户注册赠送积分活动 695849