EnICO – Energy efficient Industry Cluster Optimization

EnICO â 节能产业集群优化

• 批准号: 439187891
• 负责人: Professor Dr. Oliver Rose
• 金额: --
• 依托单位: Professur für Technische Logistik
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/439187891?language=en
• 关键词: EnICO; Energy; efficient; Industry; Cluster

Limited resources, rise in energy costs and continuous change in government policies, e.g. exit from nuclear and fossil fuel energy, exert pressure on the economical division of industrial companies. In addition, the call for higher sustainability, the reduction in environmental pollution and efficiency in resource consumption aim for the improved handling of energy resources. In this context, the focus of national support programs pursue the increasing efficiency of end-use energy consumption, especially at the costumer level. In contrast, the potentials of energetic interdependencies between industrial companies within a local cluster (e.g. industrial parks) do not get on closer examination so far.Consequently, the primary objective of the research project comprise of both modelling and optimization of energetic and substantial cooperation between productive companies within a cluster. Following the resource efficiency concept, the optimization of industrial clusters has two main objectives – maximization of cluster internal resource utilization as well as minimization of external resource demand. Therefore, the development of an optimization tool (EnICO generator) is planned which will support the digital mapping, self-optimization with the goal of energy efficiency and in the long-term, the planning of ideal eco-industrial parks. The optimization problem is discrete-continuous and a dynamic combination of simulation and optimization is necessary. One aim is to identify the interrelations, which may have positive effects on energy resource demand within the cluster. The second aim is to reach an energetic-optimal cooperation behaviour, which increases the energy efficiency. This is based on the generation of solutions for solving the set of problems, which comprises time-discrete, dynamic and sequence-relevant allocation of restricted resources. For this, the identification as well as parametrization of relevant correlations, the modelling of optimization objective criteria and the definition of control algorithm are essential. The overall project objective leads to a high complexity of the optimization model. One of the innovative and advantageous factors of the proposed project will be the mapped level of abstraction, which is essential to model and optimize the efficiency of energetic interrelations between individual enterprises. On the other hand, intelligent cluster optimization is ensured by implementation of a multi agent system (MAS). This MAS systematically combines “optimization knowledge” to rule sets, based on the approach of Machine Learning. As long-term objective, it should be possible to generate a specific optimal model. For this, solely the developed set of rules automatically generate appropriate interrelations into a base model of an industrial cluster.

有限的资源、能源成本的上升以及政府政策的不断变化，例如退出核能和化石燃料能源，给工业公司的经济分工带来了压力。此外，要求提高可持续性、减少环境污染和提高资源消耗效率的目的是改善能源的处理。在这方面，国家支持方案的重点是提高终端能源消费的效率，特别是在消费者一级。与此相反，地方集群（例如工业园区）内的工业企业之间的相互依存潜力尚未得到更深入的研究。因此，本研究项目的主要目标是对集群内生产企业之间的积极和实质性合作进行建模和优化。遵循资源效率的概念，产业集群的优化有两个主要目标--集群内部资源利用率最大化和外部资源需求最小化。因此，计划开发一种优化工具（EnICO发电机），该工具将支持数字测绘，以能源效率为目标的自我优化，并从长远来看，规划理想的生态工业园区。优化问题是离散-连续的，仿真和优化的动态结合是必要的。目的之一是确定可能对集群内能源需求产生积极影响的相互关系。第二个目标是达到能量最优的合作行为，提高能源效率。这是基于解决一系列问题的解决方案的生成，其中包括有限资源的时间离散，动态和序列相关的分配。为此，识别以及参数化的相关性，建模的优化目标标准和控制算法的定义是必不可少的。总体项目目标导致优化模型的高度复杂性。拟议项目的创新和有利因素之一将是映射的抽象层次，这对于建模和优化单个企业之间的能量相互关系的效率至关重要。另一方面，智能集群优化的多代理系统（MAS）的实施，确保。该MAS系统地结合“优化知识”的规则集，基于机器学习的方法。作为长期目标，应该有可能生成特定的最佳模型。为此，仅仅是开发的一套规则自动生成适当的相互关系到一个基本模型的产业集群。