面向可持续制造的车间能耗建模及优化技术研究

项目名称： 面向可持续制造的车间能耗建模及优化技术研究

项目编号： No.71271068

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 管理科学

项目作者： 闫纪红

作者单位： 哈尔滨工业大学

项目金额： 56万元

中文摘要： 可持续制造是制造业在可持续发展战略下的必然趋势，在制造过程中强调最小能耗的最大效益，与传统制造业所追求的时间、质量、成本效益最大化的目标有本质区别。本项目对车间层能耗建模、优化及其实施等关键技术展开研究，提出在能耗建模过程中必须考虑加工参数、以及设备性能对能耗影响的观点；建立了包括设备加工、调整、传输、等待、以及公共设施能耗的车间综合能耗模型，基于统计学和人工智能方法给出了设备性能与能耗的关联关系和作用规律；基于（火用）损理论，建立了能源转化效率模型，构建了综合能耗、能效的目标函数模型，揭示设备生产效率与能耗、能效之间的关系，提出基于人工智能的车间能耗优化调度方法。建立了基于能耗预测、监控、仿真、管控一体化的体系结构。研究工作是实现设备能耗优化及预测、提高能源效率、有效利用资源以及降低企业碳排放量的重要支撑技术，具有重大的理论研究意义与应用价值。

中文关键词： 可持续制造；能耗建模；机床能耗；车间能耗；能耗优化

英文摘要： Sustainable manufacturing is an inevitable trend of manufacturing sector in the context of sustainable development strategy. It aims at maximum effectiveness with minimum energy consumption in manufacturing process, which is different in essence with traditional manufacturing industries whose solitary goal is simply in the pursuit of benefit maximization on time, quality, and cost. This project commenced with research in key technologies such as energy consumption modeling on workshop level, its optimization and implementation, proposed that different processing technics should be taken into account as an influence factor of equipment energy consumption when modeling, built up a workshop energy consumption model including equipment processing, adjustment, transfer, wait, and energy consumption of public facilities, and gave the energy consumption characteristics of worn equipment.The project also put forward a multi-dimensional objective function model which took factors, as energy consumption, production costs, production efficiency, etcetera, into consideration and advanced the constraints which took elements such as resource, limited cache, task flexibility and the influence on environment exerted in the course of workshop production.Based on exergy analysis, the model of energy conversion efficiency and obj

英文关键词： sustainable manufacturing；energy consumption model；energy consumption for machinery；energy consumption for workshop；energy consumption optimization