A phenomenological model of cellulose production processes presents limitations due to the presence of species and chemical reactions of complex computational representation. Modeling based on machine learning techniques is an alternative to overcome this drawback. This paper addresses the Gaussian process regressor (Kriging) method to model the oxygen delignification process in one of the largest pulp production plants of the world. Different correlation models were used to evaluate this method; furthermore, an optimization routine, based on the constrained optimization by linear approximation method, was coupled to model to minimize the objective function, which is based on the input cost. Results have shown the good performance of using a combined Kriging method with optimization routines in the non-linear industrial processes to obtain a representative model capable of providing optimized operating scenarios. A reduction of 36.5% in consumption of NaOH was obtained, while required restrictions are obeyed.

中文翻译：

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使用基于Kriging的算法对氧气脱木素过程进行建模

由于种类的存在和复杂的计算表示的化学反应，纤维素生产过程的现象学模型提出了限制。基于机器学习技术的建模是克服此缺点的替代方法。本文介绍了高斯过程回归器（Kriging）方法，以对世界上最大的纸浆生产厂之一中的氧气脱木质素过程进行建模。使用不同的相关模型评估该方法。此外，将基于线性逼近方法的约束优化的优化例程与模型耦合，以基于输入成本最小化目标函数。结果表明，在非线性工业过程中结合使用Kriging方法和优化例程来获得能够提供优化操作方案的代表性模型具有良好的性能。NaOH的消耗减少了36.5％，同时遵守了必要的限制。