工業廢水的處理方法可以經由複雜的化學反應導致的非線性行為所解釋。混凝過程因受不同因素影響，對於優化而言具挑戰性。廢水的頻繁變化使實驗室實驗很難精確評估混凝劑量，其實驗耗時、花成本以及需要高技能的技術人員。本研究發展出一種基於人工智慧的方法預測去銅的混凝劑用量，以改善某一個工業廢水處理廠排放的廢水品質。為了決定運作的顯著因子，進行田口式實驗設計。對於用在本研究的毎個混凝劑，利用人工神經網路發展其介於單一隠藏層和兩個隠藏層間不同的智慧模擬與序列模型。藉由統計驗證了所提出的模型性能，就時間效率而言預測模型的成效可期。本研究結果有助於提供一種資料驅動方法，使決策者能夠根據廢水特性，通過豐富的資料與實驗室實驗取得適當的混凝劑量來去除銅

The method of treating waste water in industry is explained by complex chemical reactions that result in nonlinear behaviors. The coagulation process is challenging to optimize because it is affected by different factors. The frequent changes in waste water make it difficult for laboratory experiments to be used to accurately assess the coagulant amount, and such experiments are time-consuming, costly and requires highly skilled technicians. This study develops an artificial intelligent based approach to predict the dosage of coagulants for Cu removal to improve the outgoing waste water quality in an industrial waste water treatment plant. To determine the significant factors of the operation, Taguchi design of experiment was developed. Different intelligent simultaneous and sequence models between single-hidden-layer and two-hidden-layer for each coagulant used in this study were developed by using artificial neuron networks. The proposed model performance is validated by statistics. The prediction models give promising result in terms of time efficiency. The outcomes of the study contributes to provide a data-driven approach that enables the decision maker to get proper coagulants dosage for Cu removal based on the waste water characteristics with enrichment of data and laboratory experiments.

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