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Probabilistic modeling and dynamic optimization for performance improvement and risk management of plant-wide operation

机译：概率建模和动态优化，以改善整个工厂范围内的绩效和风险管理

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摘要

This study presents a novel algorithm for constructing a probabilistic model based on historical operation data and performing dynamic optimization for plant-wide control applications. The proposed approach consists of applying a self-organizing map (SOM) for identifying representative plant operation modes based on a discounted infinite horizon cost and approximate dynamic programming techniques for learning an optimal policy. A quantitative measure for risk is defined in terms of transition probability, and a systematic guideline for striking balance between risk and profit in decision making is provided with a mathematical proof. The efficacy of the proposed approach is illustrated on an integrated plant consisting of a reactor, a storage tank, and a separator with a recycle loop and Tennessee Eastman challenge problem. The algorithm is useful for learning an improved policy and reducing risk in plant operation when a plant-wide model is difficult to obtain and uncertainties affect operation performance significantly.
机译：这项研究提出了一种新颖的算法，该算法可基于历史操作数据构建概率模型并为全厂范围控制应用执行动态优化。所提出的方法包括应用自组织图（SOM），用于基于打折的无限期成本和用于学习最优策略的近似动态编程技术来确定代表性的工厂运营模式。根据转移概率定义了风险的定量度量，并为决策中的风险与利润之间取得平衡提供了系统的指南，并提供了数学证明。在由反应器，储罐和具有再循环回路的分离器以及田纳西州伊士曼挑战问题组成的综合工厂中说明了该方法的有效性。当难以获得整个工厂范围的模型并且不确定性显着影响运营绩效时，该算法可用于学习改进的策略并降低工厂运营中的风险。

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