Application of Artificial Neural Networks.pdf
- 1. Applications of ANNs in Wastewater Treatment ~ By Raj Shetye
- 2. ANN in Wastewater Treatment o Artificial Neural Networks (ANNs) are AI-based models used to predict, optimize, and automate wastewater treatment processes. o They help in handling complex, nonlinear relationships between multiple parameters affecting treatment efficiency. Why Use ANN in Wastewater Treatment? ▪ Traditional models are limited – ANN can model complex interactions between multiple variables. ▪ Reduces trial-and-error experiments – Saves time and cost. ▪ Handles real-time data – Helps in quick decision-making for process control. How Does ANN Work in Wastewater Treatment? ➢ Input Variables: pH, COD, BOD, TKN, flow rate, pollutants, etc. ➢ ANN Model Training: Learns patterns from past treatment data. ➢ Output Prediction: Estimates treatment efficiency, pollutant removal, and process optimization.
- 3. Benefits & Applications of ANN in Wastewater Treatment Key Benefits of Using ANN: ❖ Higher Accuracy – Predicts pollutant removal efficiency better than conventional models. ❖ Process Optimization – Reduces energy, chemical, and operational costs. ❖ Real-Time Monitoring – Helps adjust treatment parameters dynamically. ❖ Scalability – Can be applied to both small and large treatment plants. Applications in Wastewater Treatment: ❑ Biological Treatment: Predicting COD/BOD removal efficiency. ❑ Chemical Treatment: Optimizing chemical dosing for pollutant removal. ❑ Membrane & Adsorption Technologies: Predicting membrane fouling and adsorption efficiency. ❑ Advanced Oxidation Processes (AOPs): Enhancing degradation of persistent pollutants.
- 4. Artificial intelligence models for predicting the performance of biological wastewater treatment plant in the removal of Kjeldahl Nitrogen from wastewater (D. S. Manu, Arun Kumar Thalla) Key Applications in WWTP: ❑ Prediction of Effluent Kjeldahl Nitrogen (TKN) using influent parameters (pH, COD, TS, FA, AN, TKN). ❑ Optimization of Biological Treatment Process – Enhancing nitrification & denitrification efficiency. ❑ Real-Time Monitoring & Decision Support – AI models detect inefficiencies & optimize operations. Comparison: SVM vs. ANFIS: ❖ SVM Model: More accurate, better generalization, lower error (RMSE). ❖ ANFIS Model: Gbell MF better than Trapezoidal MF, but less accurate than SVM. Practical Benefits: ➢ Higher WWTP Efficiency – AI models optimize treatment processes. ➢ Cost Reduction – Less energy consumption, lower operational costs. ➢ Regulatory Compliance – Accurate nitrogen removal predictions ensure discharge limits are met. ➢ Lower Experimental Costs – Virtual simulations replace costly lab trials. Conclusion: ANN-based models, especially SVM, improve nitrogen removal efficiency, making WWTPs smarter & more cost-effective.
- 5. ANN based modelling of hydrodynamic cavitation processes: Biomass pre-treatment and wastewater treatment (N.V. Ranade et al) Key Applications in Hydrodynamic Cavitation (HC) Wastewater Treatment: ❑ Prediction of Pollutant Degradation – ANN models simulate dichloroaniline (DCA) removal in wastewater. ❑ Optimization of HC Reactor Performance – ANN models analyze the effect of cavitation reactor scale and number of passes on degradation efficiency. ❑ Real-Time Process Control – AI-driven models improve scalability and efficiency of HC-based water treatment. ANN Model Performance: ❖ Trained using limited experimental data – Models accurately predicted degradation trends. ❖ Extrapolation Ability – Successfully predicts degradation behavior for larger HC reactors but shows limitations in time-based predictions. Practical Benefits: ➢ Enhanced Wastewater Treatment Efficiency – AI optimizes pollutant removal via HC. ➢ Scalability for Industrial Applications – ANN models help design reactors for large-scale treatment. ➢ Reduced Experimental Costs – Virtual simulations replace expensive lab trials. Conclusion: ANNs effectively model and optimize HC-based wastewater treatment, improving pollutant degradation and reactor scalability.