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.

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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

随着激烈的市场竞争，建立精确，可靠的性能预测模型以最低的成本进行控制和优化已成为工业生产的趋势。本文提出了一种最小成本的建模方法来开发新型非线性工业过程的性能预测模型。这种方法的核心思想是将有用的信息迁移到多个旧的和类似的流程上，以开发新的流程模型。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中的最少数据。为了获得用于组合多个旧的和相似的过程模型的最佳权重，贝叶斯模型平均法用于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体设计（NLHD）来收集有关模型迁移的新非线性过程所需的最小数据。最后，我们将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，该方法可以在新过程量方面以最小的成本开发出精确的压缩机模型。数据。执业者注意 －过程优化控制和状态监视对于工业过程的稳定性和经济运行至关重要，而它们的基础是快速建立准确而可靠的过程性能预测模型。用于开发过程性能预测模型的传统方法通常需要大量的复杂计算和丰富的过程数据，这既费时又费钱。特别地，这些方法仅关注要建模的当前过程，而忽略了现有的和类似的过程信息，浪费了过程信息。本文提出了一种用于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有关多个相似现有过程的信息，以辅助新过程的建模，从而降低新过程的建模成本。特别，设计了包括贝叶斯模型平均在内的多模型迁移策略，以将有用的信息从类似过程迁移到新过程。嵌套的拉丁超立方体设计（NLHD）用于收集有关新非线性过程的必要最小数据。通过将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获得准确的性能预测模型。