GOALI: Long-term Planning of Plant-wide Recovery and Treatment Options

摘要：Andreas Linninger和Richard Colberg研究所：伊利诺伊大学芝加哥分校提案编号：0090370多用途批量生产场地的废物减少和污染预防工作规划涉及到资源的谨慎使用，如原材料，质量分离剂和能源，以支持化学品的生产。 这是一个动态的开放式问题，因为战略应预见到未来业务活动的新趋势、现有制造做法以外的新技术的可用性或环境问题。 不确定性源于一系列连续的生产活动，每次持续数周到数月。 工厂范围内的废物管理通常建立在现有的基础设施上，如溶剂回收厂、焚化炉、化学处理技术和生物处理。 在不确定性的存在下的改造情况与在大宗商品业务中使用的专用工厂中占主导地位的确定性基层设计方法形成对比。 本研究包括制定一个固定的规划范围内的工厂范围内的废物管理的最佳设计方法。 由于问题的多面性，计划进行一个协同的学术-工业合作（GOALI项目）。PI计划开发一种他们称之为“组合过程合成”的新技术，该技术使用两阶段解决方案。 在第一阶段，上层结构生成，基于知识的推理机制搜索技术上可行的回收和处理方案的上层结构。 非线性平衡和本构方程的强制执行，以保障不断变化的状态任务网络的一致性。 在第二步中，上层建筑优化根据工厂特定的物流、容量和排放限制确定工厂范围内的废物管理策略。 最佳战略还建议作出投资决定，如新的再循环技术或处理办法，以同时提高经济和生态绩效或达到更严格的环境标准。 组合过程综合方法的主要优点在于它能够通过知情搜索和数学规划的组合来提供开放式问题的算法解决方案。

ABSTRACTPI: Andreas Linninger and Richard Colberg Institution: University of Illinois at chicagoProposal Number: 0090370Planning of waste reduction and pollution prevention efforts for multi-purpose batch manufacturing sites is concerned with the mindful use of resources such as raw materials, mass separating agents and energy to support the production of chemicals. It is a dynamic open-ended problem, since strategies should anticipate emerging trends in future business operations, availability of new technologies beyond current manufacturing practices or environmental concerns. Uncertainty stems from a continuous sequence of production campaigns, each lasting from weeks to months. Plant-wide waste management typically builds on existing site infrastructure such as solvent recovery plants, incinerators, chemical treatment technologies, and biological treatment. The retrofit situation in the presence of uncertainty contrasts the deterministic grass-roots design approach predominant in dedicated plants used in the bulk commodity business. This research consists of developing optimal design methodology for plant-wide waste management for a fixed planning horizon. Due to the multi-faceted nature of the problem, a synergistic academic-industrial collaboration (GOALI project) is planned.The PIs plan to develop a new technique they call "combinatorial process synthesis," which uses a two-phase solution approach. In phase one, superstructure generation, a knowledge-based reasoning mechanism searches for a superstructure of technically feasible recovery and treatment options. Non-linear balance and constitutive equations are enforced to safeguard the consistency of the evolving state task network. In step two, superstructure optimization identifies plant-wide waste management strategies subject to plant-specific logistic, capacity and emission constraints. Optimal strategies also suggest investment decisions such as new recycle technologies or treatment options to simultaneously increase economic and ecological performance or to address more stringent environmental standards. The major advantage of the combinatorial process synthesis method lies in its ability to render an algorithmic solution to open-ended problems via a combination of informed search and mathematical programming.