GALINI: Global ALgorithms for mixed-Integer Nonlinear optimisation of Industrial systems

在2015年的巴黎气候大会上，195个国家一致认为，全球温室气体应尽快达到峰值，此后各国应迅速减少排放。因此，加工工业必须在保持消费者服务的同时减少能源消耗并提高效率。在工程、计算机科学和数学的界面上的下一代决策软件对于这些未来的高效系统至关重要。最先进的计算软件包已经在流程工业中成为常规;几乎每个大公司都使用仿真和优化来模拟不同模式下的生产，包括：连续，批量和半连续生产系统。但更有效的工业系统需要同时考虑许多紧密集成的子系统，这些子系统以指数方式增加复杂性并需要许多时间/空间尺度;由此产生的决策问题可能无法用当前技术解决。提高效率也可能危及安全：效率所需的过程集成意味着过程之间的热量交换，并可能通过在工厂中传输干扰来破坏安全预防措施。在此研究期间，我们建议开发GALINI，新的决策软件，构建和部署下一代过程优化工具，处理组合复杂性，不同的时间/空间尺度和安全考虑。GALINI项目提出了优化算法的逐步变化，这些算法可立即适用于过程系统工程（PSE）中的效率挑战：安全运行间歇式反应器，改造热交换器网络，中间混合以及集成规划和调度。我们将在开源平台Pyomo上免费发布我们的软件，并建立一个国际用户社区。GALINI的主要研究目标是开发优化软件，推动PSE能效应用的计算可处理性边界。流程工业中的有效优化软件回答：我们如何才能最好地实现明确的工程目标？考虑到现有工厂布局或生产特定产品的合同义务等限制，该软件通过定量比较不同选项的影响并确定最佳决策来支持新的工程设计。GALINI对设计特别感兴趣：我们应该如何建造新设施或修改现有设施，以最大效率实现我们的设计目标？商业建模软件如AspenTech和gPROMS代表了流程工业决策的最新水平。实际上，流程工业中的每个大公司都使用这些软件工具，因为模拟或优化的输出可以在最小的日常运营中断的情况下实施，并且可以在短至6-12个月的投资回收期内实现节省。GALINI将为混合整数非线性规划开发确定性全局优化软件，这是一种与能源效率和过程系统工程高度相关的优化问题。能源效率实例可能表现出非凸性的数学特性，即具有许多局部最优解;全局优化在数学上保证了最佳的过程工程解决方案。GALINI提出了算法的转型转变，创造性地重新想象了通常应用于这种类型的优化问题的核心分治算法。我们的方法是免费发布GALINI给用户，包括那些在过程工业中，宣传软件，展示其实用性，并建立一个用户社区，将反馈到软件开发。

At the 2015 Paris climate conference, 195 countries agreed that global greenhouse gases should peak as soon as possible and that countries should thereafter rapidly reduce their emissions. The process industries must therefore reduce their energy consumption and increase efficiency while maintaining consumer services. Next generation decision-making software at the interface of engineering, computer science, and mathematics is critical for these efficient systems of the future. Already, state-of-the-art computational packages are routine in the process industries; practically every major company uses simulation and optimisation to model production in different modes including: continuous, batch, and semi-continuous production systems. But more efficient industrial systems require simultaneously considering many tightly integrated subsystems which exponentially increase complexity and necessitate many temporal/spatial scales; the resulting decision making problems may not be solvable with current techniques. Increasing efficiency may also jeopardise safety: the process integration required for efficiency implies interchanging heat between processes and may damage safety precautions by transferring disturbances across a plant.During this fellowship, we propose to develop GALINI, new decision-making software constructing and deploying next generation process optimisation tools dealing with combinatorial complexity, disparate temporal/spatial scales, and safety considerations. The GALINI project proposes step-changes in optimisation algorithms that are immediately applicable to efficiency challenges in process systems engineering (PSE): safely operating batch reactors, retrofitting heat-exchanger networks, intermediate blending, and integrating planning and scheduling. We will freely release our software on open-source platform Pyomo and build an international user community.The primary GALINI research aim is to develop optimisation software that pushes the boundary of computational tractability for PSE energy efficiency applications. Effective optimisation software in the process industries answers: How can we best achieve a definite engineering objective? Given constraints such as an existing plant layout or a contractual obligation to produce specific products, the software supports novel engineering by quantitatively comparing the implications of different options and identifying the best decision. GALINI is particularly interested in design: How should we build new facilities or modify existing ones to achieve our design goals with maximum efficiency?The state-of-the-art in decision making for the process industries is represented by commercial modelling software such as AspenTech and gPROMS. Practically every major company in the process industries uses these software tools since the outputs of the simulation or optimisation can be implemented with minimal day-to-day operational disruption and savings can be realised with a payback time as short as 6-12 months. GALINI will develop deterministic global optimisation software for mixed-integer nonlinear programs, a type of optimisation problem highly relevant to energy efficiency and process systems engineering. Energy efficiency instances may exhibit the mathematical property of nonconvexity, i.e. have many locally optimal solutions; global optimisation mathematically guarantees the best process engineering solution. GALINI proposes transformational shifts in algorithms that creatively reimagine the core divide-and-conquer algorithm typically applied to this type of optimisation problem. Our approach is to freely release GALINI to users including those in the process industries, publicise the software, demonstrate its utility, and build a user community that will feed back into software development.