現今工業發展蓬勃，工廠所產生的廢水一直是非常困擾的問題，其中酸水汽提的例子也很多，文獻記載眾多依照不同條件的解決方案，除了廢水問題的解決，這當中的成本也是廠商所重視的，因此會希望製程當中的參數調整能降低整體的能耗。但在研究能耗的問題之前，我們通常需要眾多的物流數據，例如進料溫度、進料流量，出料溫度、出料濃度以及所需的能耗是多少，有了這些資料才能夠找到影響能耗的關鍵參數為何。

但這些數據往往在量測上還有人力的成本問題、即時性的問題以及準確性的疑慮，因此，本研究以酸水汽提製程為例，先利用Aspen Plus軟體生成數千筆數據，結合循環網路(Cycle network)與差分進化演算法(Differential evolution algorithm ,DEA )訓練人工神經網路(Artificial Neural Network , ANN ) 模型，最後預測未知參數。利用DEA和Cycle network的主要目的在於能夠利用已知參數找到未能從工廠量測到的數據(如:板效率)，藉此以數學模型取代物理模型，能夠盡速找到欲求的參數，並減少大量的人力，在未來有望利用此模型繼續研擬如何改變操作條件來降低處理廢水甚至是其他製程的成本問題。

Nowadays, the industry is developing vigorously, and the wastewater produced by factories has always been a very annoying problem. Among them, there are many examples of sour water stripping. There are many solutions according to different conditions in the literature. In addition to the solution of wastewater problems, the cost of this is also valued by businessmen. Therefore, it is hoped that the adjustment of the parameters in the process can reduce the overall energy consumption. But before studying energy consumption, we usually need a lot of logistics data, such as feed temperature, feed flow, discharge temperature, discharge concentration, and the required energy consumption. Only with these data can we find out the key parameters that affect energy consumption.

However, these data often have labor cost issues, real-time issues and accuracy doubts in the measurement. Therefore, this study uses Aspen Plus to generate thousands of data, combined with artificial neural networks (ANN) to find the relationship between variables and predict unknown parameters. It also uses the ideas of Differential evolution and Cycle network to find data that cannot be measured from the factory (such as stage efficiency), so that it can replace the physical model with a mathematical model, find the desired parameters as soon as possible, and reduce a lot of human resources in the future, it is expected to use this technology to continue to study how to change the operating conditions to reduce the cost of wastewater treatment and even other processes.

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