Document Details

Document Type : Thesis 
Document Title :
OPTIMIZING WASTEWATER TREATMENT PLANT PERFORMANCE BY USING ARTIFICIAL NEURAL NETWORK MODELING
تحسين اداء محطات المعالجة باستخدام الشبكات العصبية الاصطناعية
 
Subject : Faculty of Meteorology, Environment and Arid Land Agriculture 
Document Language : Arabic 
Abstract : Wastewater treatment plant plays important role in removing chemical to protect the environment. However, there is a strong pressure on wastewater treatment plants to meet the regulated standers of treatment [1]. As a result, it is necessary to optimize the operation performance. Automation operation in WWTP can play a major role in optimizing performance of treatment plants. The aim of this study is to develop a computer model to measure some of the operation parameters at each stage of treatment [1]. The fluctuation of the wastewater characterization and flow rate can greatly influence the performance of the WWTP. Monitoring and controlling operation in real time pose a problem for the plant. The BOD of the incoming wastewater is not readily available and can’t be measure directly for instance BOD value. In this study the use of artificial neural network to predict the value of BOD. The model can respond to changes at the characteristics of incoming waste water by changing the operations parameters [2]. The study of the treatment plant in Jeddah Industrial City was based on operating indicators during 3 years for 600 samples. The study indicated that the application of neural networks gave satisfactory results in the prediction of the performance of the station and some values of operation with the correlation coefficient (R). For the benchmark index for sludge values in the sedimentation basin reached 90%. As a result, the modeling tool provides an effective and important tool in understanding and simulating the behavior of non-linear phenomena in wastewater treatment processes, and the operation of treatment plants. Predict the operational problems that may be exposed to the terminal and give it a mechanism to solve it through modeling on the MATLAB program. 
Supervisor : Dr. Asaad Abu Azizah 
Thesis Type : Master Thesis 
Publishing Year : 1442 AH
2020 AD 
Added Date : Saturday, September 12, 2020 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
فهد صالح الجهنيAlgohani, Fahad SalihResearcherMaster 

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