News Updates Friday 22nd Mar 2019 :
  • Welcome to INPRESSCO, world's leading publishers, We have served more than 10000+ authors
  • Articles are invited in engineering, science, technology, management, industrial engg, biotechnology etc.
  • Paper submission last date of March/April 2019 is 15 March 2019, Submit online or at
  • Our journals are indexed in NAAS, University of Regensburg Germany, Google Scholar, Cross Ref etc.
  • DOI is given to all articles

Optimal Alum Dosage Prediction Required to Treat Effluent Water Turbidity using Artificial Neural Network

Author : Jabbar H. Al-Baidhani and Mohammed Adnan Alameedee

Pages : 1552-1558
Download PDF

The present study was conducted for the purpose of simulation of some units that was used intensively in a conventional water treatment plant. A pilot plant used to test the simulated raw water by mixing kaolinite clay with tap water and treated with difference alum dosage. The simulation tool that was used recently based on artificial intelligence and experimental tests, which is the neural network (ANN). The ANN was used to simulate and predict the required alum dosage for treated the turbidity in raw water. The experimental work was studied in a pilot plant scale, where a rig was designed and manufactured to simulated these treatment units. The synthetic turbidity was used to create the turbidity water that was used in this work. The experimental study was consisted of four runs with different inlet flow rate; each run contained five different influent turbidity sets (25, 50, 75, 100, 150) NTU and ten series of alum dosage varies form (5 – 50) mg/l for each turbidity set. The collected data for the neural network was about 200 set of data. The inverse model had a nine input parameters and one-output parameters, which is the alum dosage. The correlation coefficient of this model was 0.96, while the error indices were 4.1 mg/l, 2.84 mg/l, and 13.3% for RMSE, MAE, and MAPE respectively.

Keywords: Artificial Intelligence; Artificial Neural Network (ANN); Inverse Model; Correlation Coefficient (R); Root Mean Square Error; Mean Absolute Error; and Mean Absolute Percentage Error

Article published in International Journal of Current Engineering and Technology, Vol.7, No.4 (Aug-2017)

Call for Papers
  1. IJCET- March/April 2019 Issue

    Submission Last Date
    22 March
  2. DOI is given to all articles
  3. Current Issue
  4. IJTT-March-2019
  5. IJAIE-March-2019
  6. IJCSB-March-2019
  • Inpressco Google Scholar
  • Inpressco Science Central
  • Inpressco Global impact factor
  • Inpressco aap

International Press corporation is licensed under a Creative Commons Attribution-Non Commercial NoDerivs 3.0 Unported License
©2010-2018 INPRESSCO® All Rights Reserved