Parallelising Mixed-Integer Optimisation: Energy Efficiency Applications

并行混合整数优化：能源效率应用

• 批准号: EP/P008739/1
• 负责人: Ruth Misener
• 金额: $ 12.84万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2017
• 资助国家: 英国
• 起止时间: 2017 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP008739%2F1
• 关键词: Parallelising; Mixed; Integer; Optimisation; Energy

Mathematical models for optimal decisions often require both nonlinear and discrete components. These mixed-integer nonlinear programs (MINLP) form an important class of optimisation problems of pressing societal need. For example, MINLP is necessary for optimising the energy use of large industrial plants, for integrating renewable sources into energy networks, for biological and biomedical design, and for countless other applications. The first MINLP algorithms and software were designed by application engineers. While these efforts initially proved very useful, scientists, engineers, and practitioners have realised that a transformational shift in technology will be required for MINLP to achieve its full potential.As an example of the importance of MINLP, consider that many industrial processes involve heating and cooling liquids. With the present day focus on reducing CO2 emissions, e.g. the UK Climate Change Act 2008, reusing excess process heat becomes ever more important and a major challenge is increasing industrial plant efficiency via heat integration. Heat exchanger network (HEN) synthesis is most naturally formulated as a mixed-integer nonlinear optimisation problem (MINLP). Using an optimisation framework can result in tremendous energy and cost savings. In 2009, the South Korean refining company S-Oil estimated £28M annual savings at a single plant using a commercial optimisation package, AspenTech Energy Analyzer. But these are not the only gains available. Heat exchanger network synthesis is a nonconvex nonlinear optimisation problem with many local optima; we estimate additional possible savings on the order of 10% via developing better optimisation algorithms.Deterministic global optimisation of mixed integer nonlinear programs (MINLP) may effectively design energy efficient networks, but current MINLP technology for this problem class is limited by nonconvex nonlinear heat transfer functions and the many isomorphic possibilities of routing streams to heat exchangers. Parallelisation is attractive, but the naïve design of current parallelisation strategies is also inappropriate because effective tree exploration requires extensive inter-node communication. This proposal aims to develop novel internode communication strategies for MINLP branch-and-cut algorithms with a target of effectively addressing industrially-relevant energy efficiency optimisation problems.This proposal is highly relevant to the 680k people working in the UK energy sector. This proposal falls under the EPSRC Engineering and Manufacturing the Future themes; MINLP is highly relevant to industrial design problems. The two related sub-themes are Sustainable Industrial Systems with a related research area of Energy Efficiency (EPSRC Research Action: Grow) and also Manufacturing Informatics with a related research area of Mathematical Aspects of Operational Research (EPSRC Research Action: Maintain). This proposal is also tightly linked to the EPSRC Working Together priority; the team includes the PI, the PDRA, a mathematician, a software company, and a consortium of process engineers. Since moving to the UK in 2012, the PI has attracted international attention for her MINLP contributions as evidenced by her 2 paper awards in 2013 and 2014; this EPSRC First Grant will establish her as researcher with a reliable track record of linking optimisation theory and practice.

用于最优决策的数学模型通常需要非线性和离散组件。这些混合整数非线性规划（MINLP）形成了一类重要的优化问题的迫切社会需求。例如，MINLP对于优化大型工业工厂的能源使用，将可再生能源整合到能源网络中，生物和生物医学设计以及无数其他应用都是必要的。第一个MINLP算法和软件是由应用工程师设计的。虽然这些努力最初被证明是非常有用的，但科学家、工程师和从业者已经意识到，MINLP要充分发挥其潜力，需要技术的转型转变。作为MINLP重要性的一个例子，考虑到许多工业过程都涉及加热和冷却液体。随着当今对减少CO2排放的关注，例如2008年英国气候变化法案，再利用多余的过程热变得越来越重要，并且主要挑战是通过热集成来提高工业工厂效率。换热网络综合问题是一个混合整数非线性优化问题。使用优化框架可以节省大量的能源和成本。2009年，韩国炼油公司S-Oil估计，使用商业优化包AspenTech Energy Analyzer，单个工厂每年可节省2800万英镑。但这些并不是唯一的收益。换热网络综合是一个非凸非线性优化问题，存在许多局部最优解;我们估计，通过开发更好的优化算法，还可能节省大约10%。混合整数非线性规划的确定性全局优化（MINLP）可以有效地设计节能网络，但目前这类问题的MINLP技术受到非凸非线性传热函数和将流路由到热的许多同构可能性的限制。交换器.并行化是有吸引力的，但当前并行化策略的幼稚设计也是不合适的，因为有效的树探索需要广泛的节点间通信。该提案旨在为MINLP分支切割算法开发新的节点间通信策略，目标是有效解决工业相关的能源效率优化问题。该提案与英国能源部门的68万人高度相关。该提案福尔斯属于EPSRC未来工程和制造主题; MINLP与工业设计问题高度相关。两个相关的子主题是可持续工业系统与能源效率（EPSRC研究行动：增长）的相关研究领域，以及制造信息学与运筹学的数学方面（EPSRC研究行动：维护）的相关研究领域。该提案也与EPSRC Working Together优先级紧密相关;该团队包括PI，PDRA，数学家，软件公司和工艺工程师联盟。自2012年移居英国以来，PI因其在MINLP的贡献而引起了国际关注，2013年和2014年的2篇论文获奖证明了这一点; EPSRC第一笔赠款将使她成为研究人员，并具有可靠的优化理论和实践联系记录。

项目成果

Heuristics with performance guarantees for the minimum number of matches problem in heat recovery network design

热回收网络设计中最小匹配数问题的性能保证启发法

• DOI: 10.1016/j.compchemeng.2018.03.002
• 发表时间: 2018
• 期刊: Computers & Chemical Engineering
• 影响因子: 4.3
• 作者: Letsios D

Piecewise parametric structure in the pooling problem: from sparse strongly-polynomial solutions to NP-hardness.

池化问题中的分段参数结构：从稀疏强多项式解到 NP 难度。

• DOI: 10.1007/s10898-017-0577-y
• 发表时间: 2018
• 期刊: an international journal dealing with theoretical and computational aspects of seeking global optima and their applications in science, management and engineering
• 作者: Baltean-Lugojan R

Symmetry Detection for Quadratically Constrained Quadratic Programs Using Binary Layered Graphs

使用二元分层图进行二次约束二次规划的对称性检测

• DOI: 10.48550/arxiv.1712.05222
• 发表时间: 2017
• 作者: Kouyialis G

Satisfiability modulo theories for process systems engineering

• DOI: 10.1016/j.compchemeng.2018.03.004
• 发表时间: 2018-05
• 期刊: Comput. Chem. Eng.
• 作者: Miten Mistry;A. C. D'Iddio;M. Huth;R. Misener

Reprint of: Heuristics with performance guarantees for the minimum number of matches problem in heat recovery network design

转载：热回收网络设计中最小匹配数问题的具有性能保证的启发式方法

• DOI: 10.1016/j.compchemeng.2018.10.015
• 发表时间: 2018
• 期刊: Computers & Chemical Engineering
• 影响因子: 4.3
• 作者: Letsios D