Afshar Puya, Brown Martin, Maciejowski Jan, Wang Hong
Control Systems Centre, School of Electrical and Electronic Engineering, University of Manchester, Manchester M13 9PL, UK.
IEEE Trans Neural Netw. 2011 Dec;22(12):2324-38. doi: 10.1109/TNN.2011.2174444. Epub 2011 Nov 29.
Reducing energy consumption is a major challenge for "energy-intensive" industries such as papermaking. A commercially viable energy saving solution is to employ data-based optimization techniques to obtain a set of "optimized" operational settings that satisfy certain performance indices. The difficulties of this are: 1) the problems of this type are inherently multicriteria in the sense that improving one performance index might result in compromising the other important measures; 2) practical systems often exhibit unknown complex dynamics and several interconnections which make the modeling task difficult; and 3) as the models are acquired from the existing historical data, they are valid only locally and extrapolations incorporate risk of increasing process variability. To overcome these difficulties, this paper presents a new decision support system for robust multiobjective optimization of interconnected processes. The plant is first divided into serially connected units to model the process, product quality, energy consumption, and corresponding uncertainty measures. Then multiobjective gradient descent algorithm is used to solve the problem in line with user's preference information. Finally, the optimization results are visualized for analysis and decision making. In practice, if further iterations of the optimization algorithm are considered, validity of the local models must be checked prior to proceeding to further iterations. The method is implemented by a MATLAB-based interactive tool DataExplorer supporting a range of data analysis, modeling, and multiobjective optimization techniques. The proposed approach was tested in two U.K.-based commercial paper mills where the aim was reducing steam consumption and increasing productivity while maintaining the product quality by optimization of vacuum pressures in forming and press sections. The experimental results demonstrate the effectiveness of the method.
降低能源消耗是造纸等“能源密集型”行业面临的一项重大挑战。一种具有商业可行性的节能解决方案是采用基于数据的优化技术，以获得一组满足特定性能指标的“优化”操作设置。这样做的困难在于：1）这类问题本质上是多标准的，因为提高一个性能指标可能会导致其他重要指标受损；2）实际系统往往表现出未知的复杂动态特性和多种相互联系，这使得建模任务变得困难；3）由于模型是从现有的历史数据中获取的，它们仅在局部有效，外推法存在增加过程变异性的风险。为克服这些困难，本文提出了一种用于相互关联过程的鲁棒多目标优化的新型决策支持系统。首先将工厂划分为串联连接的单元，以对过程、产品质量、能源消耗及相应的不确定性度量进行建模。然后使用多目标梯度下降算法根据用户的偏好信息来解决该问题。最后，将优化结果可视化，以便进行分析和决策。在实际应用中，如果考虑优化算法的进一步迭代，在进行进一步迭代之前必须检查局部模型的有效性。该方法通过一个基于MATLAB的交互式工具DataExplorer来实现，该工具支持一系列数据分析、建模和多目标优化技术。所提出的方法在英国的两家商业造纸厂进行了测试，其目的是通过优化成型和压榨部的真空压力来降低蒸汽消耗并提高生产率，同时保持产品质量。实验结果证明了该方法的有效性。
