With increasing drastic market competition, establishing an accurate and reliable performance prediction model for control and optimization at a minimum cost is a growing trend in industrial production. This article proposes a minimum-cost modeling method to develop the performance prediction model of a new nonlinear industrial process. The core idea of this approach is to migrate the useful information on multiple old and similar processes to develop a new process model. A multimodel migration strategy is proposed to migrate the useful information by combining the existing nonlinear process models and take full advantage of minimum data from the new nonlinear process. In order to obtain a set of optimal weights for combining the multiple old and similar process models, the Bayesian model averaging method is employed to estimate the contributions of each available old nonlinear process model to the new nonlinear process model. Moreover, a further experiment used nested Latin hypercube design (NLHD) to gather the necessary minimum data on the new nonlinear process for model migration. Finally, we apply the proposed minimum-cost modeling method to the new multistage centrifugal compressor in the combined cycle power plant, and the results show that the proposed method can develop an accurate compressor model at a minimal cost in terms of the amount of new process data. Note to Practitioners-Process optimal control and condition monitoring are vital for the stability and economic operation of industrial processes, and the basis of them is to quickly establish an accurate and reliable process performance prediction model. Traditional methods for developing process performance prediction models often require a large amount of complex calculations and rich process data, which is time- and cost-consuming. In particular, these methods focus only on the current process to be modeled, while ignoring the existing and similar process information, wasting process information. This article presents a minimum-cost modeling method for nonlinear industrial processes, which can make full use of information on multiple similar existing processes to assist the modeling of a new process to reduce the modeling cost of the new process. Specifically, a multimodel migration strategy including Bayesian model averaging is designed to migrate useful information from similar processes to the new process. The nested Latin hypercube design (NLHD) is employed to collect the necessary minimum data on the new nonlinear process. By applying the proposed approach to the industrial nonlinear process, it is possible to achieve the accurate performance prediction model with minimal new process data.

中文翻译：

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基于多模型迁移和贝叶斯模型平均法的非线性工业过程最小成本建模方法


随着市场竞争的日益激烈，以最小的成本建立准确可靠的性能预测模型进行控制和优化是工业生产的发展趋势。本文提出了一种最小成本建模方法来开发新的非线性工业过程的性能预测模型。这种方法的核心思想是迁移多个旧的、相似的流程上的有用信息来开发新的流程模型。提出了一种多模型迁移策略，通过结合现有的非线性过程模型来迁移有用信息，并充分利用新非线性过程的最少数据。为了获得组合多个旧的相似过程模型的一组最佳权重，采用贝叶斯模型平均方法来估计每个可用的旧非线性过程模型对新非线性过程模型的贡献。此外，进一步的实验使用嵌套拉丁超立方设计（NLHD）来收集模型迁移的新非线性过程所需的最少数据。最后，我们将所提出的最小成本建模方法应用于联合循环发电厂的新型多级离心压缩机，结果表明，所提出的方法可以在新工艺量方面以最小成本开发精确的压缩机模型数据。从业人员须知——过程优化控制和状态监测对于工业过程的稳定和经济运行至关重要，其基础是快速建立准确可靠的过程性能预测模型。 传统的过程性能预测模型开发方法往往需要大量复杂的计算和丰富的过程数据，耗时耗力。特别是，这些方法只关注当前要建模的流程，而忽略了现有的和相似的流程信息，浪费了流程信息。本文提出了一种非线性工业过程的最小成本建模方法，该方法可以充分利用多个相似的现有过程的信息来辅助新过程的建模，从而降低新过程的建模成本。具体来说，包括贝叶斯模型平均的多模型迁移策略旨在将有用信息从相似流程迁移到新流程。采用嵌套拉丁超立方设计（NLHD）来收集新非线性过程所需的最小数据。通过将所提出的方法应用于工业非线性过程，可以用最少的新过程数据实现准确的性能预测模型。