A neuro-based computing technique is used for simulation of olefin plants at industrial scale. Artificial neural networks are applied to estimate the flow rate of the main products of the olefin unit from available information in terms of flow rate of feed streams and operating condition of furnaces. The structure of the smart model is determined through a trial-and-error procedure taking the real plant information over four successive years. The proposed paradigm estimates the tonnage of the product streams by an absolute average relative deviation in the range of 0.9 % for methane to 3.14 % for propylene. Results confirmed that this smart simulation not only presents accurate predictions, but is easy to use, straightforward, and can be simply employed for optimization and control of the unit.

中文翻译：

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用于模拟工业烯烃工厂的基于神经的计算范式的设计

基于神经的计算技术用于模拟工业规模的烯烃工厂。应用人工神经网络从原料流的流速和炉子的操作条件方面的可用信息估计烯烃单元的主要产物的流速。智能模型的结构是通过连续四年获取真实工厂信息的试错程序确定的。提议的范例通过绝对平均相对偏差估计产品流的吨位，范围为甲烷的 0.9% 至丙烯的 3.14%。结果证实，这种智能模拟不仅提供了准确的预测，而且易于使用、简单明了，并且可以简单地用于优化和控制单元。