Establishment of Relationship Between Coagulant and Chlorine Dose Using Artificial Neural Network

IF 1.7 4区 工程技术 Q3 ENGINEERING, CIVIL
Dnyaneshwar Vasant Wadkar, Manoj Pandurang Wagh, Rahul Subhash Karale, Prakash Nangare, Dinesh Yashwant Dhande, Ganesh C. Chikute, Pallavi D. Wadkar
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Abstract

Multiple treatment phases are involved in a water treatment plant (WTP), but coagulation and disinfection are the most crucial for producing safe and clear water. Determining the optimal coagulant and chlorine doses in the laboratory is time-consuming and poses a significant challenge in water treatment. To streamline this process, artificial neural network (ANN) models have been developed to predict the chlorine dose based on the coagulant dose. Studies comparing various ANN models indicate that the radial basis function neural network (RBFNN) model provides excellent predictions (R = 0.999). In modeling with radial basis function neural networks (RBFNN) and generalized regression neural networks (GRNN), the spread factor was varied from 0.1 to 15 to achieve a stable and accurate model with high predictive accuracy. Employing soft computing models to define the coagulant and chlorine doses has proven highly beneficial for the management of WTPs, significantly enhancing the efficiency and accuracy of dosing predictions.

Abstract Image

利用人工神经网络建立混凝剂与氯剂量之间的关系
水处理厂(WTP)涉及多个处理阶段,但混凝和消毒是生产安全清水的最关键阶段。在实验室中确定混凝剂和氯的最佳剂量非常耗时,是水处理中的一项重大挑战。为了简化这一过程,人们开发了人工神经网络 (ANN) 模型,根据混凝剂剂量预测氯剂量。对各种人工神经网络模型进行比较的研究表明,径向基函数神经网络 (RBFNN) 模型的预测效果极佳(R = 0.999)。在使用径向基函数神经网络(RBFNN)和广义回归神经网络(GRNN)建模时,扩散因子在 0.1 至 15 之间变化,以获得稳定、准确且预测精度高的模型。事实证明,采用软计算模型来确定混凝剂和氯剂量非常有利于水处理厂的管理,大大提高了加药预测的效率和准确性。
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来源期刊
CiteScore
3.30
自引率
11.80%
发文量
203
期刊介绍: The aim of the Iranian Journal of Science and Technology is to foster the growth of scientific research among Iranian engineers and scientists and to provide a medium by means of which the fruits of these researches may be brought to the attention of the world’s civil Engineering communities. This transaction focuses on all aspects of Civil Engineering and will accept the original research contributions (previously unpublished) from all areas of established engineering disciplines. The papers may be theoretical, experimental or both. The journal publishes original papers within the broad field of civil engineering which include, but are not limited to, the following: -Structural engineering- Earthquake engineering- Concrete engineering- Construction management- Steel structures- Engineering mechanics- Water resources engineering- Hydraulic engineering- Hydraulic structures- Environmental engineering- Soil mechanics- Foundation engineering- Geotechnical engineering- Transportation engineering- Surveying and geomatics.
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