Nonlinear Progamming: Concepts, Algorithms, and Applications to Chemical Processes
Nonlinear Progamming: Concepts, Algorithms, and Applications to Chemical Processes
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DOI：
10.1137/1.9780898719383
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发表时间：
2010-01-01
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影响因子：
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通讯作者：
Biegler, Lorenz T.
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文献类型：
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作者：
Biegler, Lorenz T.
50多年来，化学工程应用一直是具有挑战性的优化问题的来源。对于许多化工过程系统，详细的稳态和动态行为现在可以通过一组丰富的详细的非线性模型来描述，并且过程设计和操作中的相对较小的变化可以导致效率、产品质量、环境影响和盈利能力的显著改善。特别是在过去的35年中，非线性规划（NLP）已成为化学过程优化不可或缺的工具。这些工具现在应用于研究和工艺开发阶段、设计阶段以及这些工艺的在线操作。最近，这些应用的范围正在扩展到覆盖更具挑战性的大规模任务，包括基于非线性动态模型优化的过程控制，以及将非线性模型纳入战略规划功能。此外，非线性规划的最新突破有助于廉价甚至在线解决大规模过程优化模型的能力，包括现代障碍方法的发展，对线搜索和信赖域策略的深入理解，以帮助全局收敛，算法开发中二阶导数的有效利用，以及最近开发和广泛使用的NLP代码的可用性，包括障碍方法[81，391，404]，序列二次规划（SQP）[161，159]和简化梯度法[119，245，285]。最后，由于有了AIMMS、AMPL和GAMS等优化建模环境以及近地天体服务器，更广泛的用户群可以使用优化的公式和解决办法。所有这些进步都对处理和解决以前认为棘手的过程工程问题产生了巨大的影响。除了在数学规划的发展，在过程系统工程的研究导致了优化建模公式，利用这些算法的进步，具体的模型结构和特点，导致更有效的solutions.This正文试图使这些最近的优化进展，工程师和从业者。工程师的优化文本通常分为两类。首先，优秀的数学编程文本（例如，[134，162，294，100，227]）强调基本属性和数值分析，但很少有与现实世界的应用相关的具体示例，并且从业者较难获得。另一方面，同样好的工程文本（例如，[122，305，332，53]）强调具有公知方法和代码的应用，但通常没有它们的基本基本属性。虽然他们的方法对工程师来说是可访问的并且非常有用，但这些文本并不能帮助更深入地理解这些方法，也不能提供有效解决大规模问题的扩展。
Chemical engineering applications have been a source of challenging optimization problems for over 50 years. For many chemical process systems, detailed steady state and dynamic behavior can now be described by a rich set of detailed nonlinear models, and relatively small changes in process design and operation can lead to significant improvements in efficiency, product quality, environmental impact, and profitability.With these characteristics, it is not surprising that systematic optimization strategies have played an important role in chemical engineering practice. In particular, over the past 35 years, nonlinear programming (NLP) has become an indispensable tool for the optimization of chemical processes. These tools are now applied at research and process development stages, in the design stage, and in the online operation of these processes. More recently, the scope of these applications is being extended to cover more challenging, large-scale tasks including process control based on the optimization of nonlineardynamicmodels, as well as the incorporation of nonlinear models into strategic planning functions.Moreover, the ability to solve large-scale process optimization models cheaply, even online, is aided by recent breakthroughs in nonlinear programming, including the development of modern barrier methods, deeper understanding of line search and trust region strategies to aid global convergence, efficient exploitation of second derivatives in algorithmic development, and the availability of recently developed and widely used NLP codes, including those for barrier methods [81, 391, 404], sequential quadratic programming (SQP) [161, 159], and reduced gradient methods [119, 245, 285]. Finally, the availability of optimization modeling environments, such as AIMMS, AMPL, and GAMS, as well as the NEOS server, has made the formulation and solution of optimization accessible to a much wider user base. All of these advances have a huge impact in addressing and solving process engineering problems previously thought intractable. In addition to developments in mathematical programming, research in process systems engineering has led to optimization modeling formulations that leverage these algorithmic advances, with specific model structure and characteristics that lead to more efficient solutions.This text attempts to make these recent optimization advances accessible to engineers and practitioners. Optimization texts for engineers usually fall into two categories. First, excellent mathematical programming texts (e.g., [134, 162, 294, 100, 227]) emphasize fundamental properties and numerical analysis, but have few specific examples with relevance to real-world applications, and are less accessible to practitioners. On the other hand, equally good engineering texts (e.g., [122, 305, 332, 53]) emphasize applications with well-known methods and codes, but often without their underlying fundamental properties. While their approach is accessible and quite useful for engineers, these texts do not aid in a deeper understanding of the methods or provide extensions to tackle large-scale problems efficiently.