Development of a Hybrid First Principles-ANN Model for the Steam Hydrator in a Calcium Looping Process

Q3 Chemical Engineering
Shreyasi Dutta, Shrinkhla, Mohamed Khalil Kallangodan, A. Gurumoorthy
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引用次数: 0

Abstract

One of the promising technologies for isolating carbon dioxide from a mixture of industrial flue gases is the calcium looping process. This process involves a reversible reaction between sorbent Calcium Oxide and Carbon Dioxide. Because sorbent loses its activity after multiple cycles, hydration step was proposed, which is another reversible reaction where deactivated sorbent is treated with steam to form Ca(OH)2, which undergoes the backward reaction to give back the regenerated sorbent. Blamey et al. (2016) developed a shrinking core model based on which, studies were carried out on a small experimental reactor. This paper aims at developing a hybrid model by combining first principles model and an ANN model for improved prediction of the conversion in hydration of calcium looping process and to scale it up for optimal operations. The hybrid model is tested for various combinations of training variables and data sets with respect to temperature and cycle number and it is found that the hybrid model indeed gives better results. The performance prediction of Hybrid modelling is compared to the individual performance prediction of the ANN model and First principles approach.
钙循环过程蒸汽水合器第一原理-神经网络混合模型的建立
从工业烟气混合物中分离二氧化碳的一种有前景的技术是钙循环工艺。该过程涉及吸附剂氧化钙和二氧化碳之间的可逆反应。由于吸附剂在多次循环后失去活性,因此提出了水合步骤,这是另一个可逆的反应,其中用蒸汽处理失活的吸附剂以形成Ca(OH)2,然后进行反向反应以回收再生的吸附剂。Blamey等人(2016)开发了一个收缩堆芯模型,在此基础上对一个小型实验反应堆进行了研究。本文旨在将第一性原理模型和人工神经网络模型相结合,开发一种混合模型,以改进对钙环化过程中转化率的预测,并扩大其规模以实现最佳操作。针对训练变量和数据集在温度和循环次数方面的各种组合,对混合模型进行了测试,发现混合模型确实给出了更好的结果。将混合建模的性能预测与人工神经网络模型和第一性原理方法的个体性能预测进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Recent Innovations in Chemical Engineering
Recent Innovations in Chemical Engineering Chemical Engineering-Chemical Engineering (all)
CiteScore
2.10
自引率
0.00%
发文量
20
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