A comparative study of clonal selection algorithm for effluent removal forecasting in septic sludge treatment plant

Area of Science:

• Environmental Engineering
• Artificial Intelligence
• Water Treatment Technology

Background:

• Effective septic sludge treatment plant (SSTP) effluent prediction is critical for infrastructure development, particularly in developing countries.
• Accurate effluent quality prediction aids in planning and constructing new SSTPs to meet environmental standards.

Purpose of the Study:

• To develop and evaluate an artificial intelligence model for predicting SSTP effluent quality, focusing on biological oxygen demand, chemical oxygen demand, and total suspended solids.
• To compare the performance of a clonal selection algorithm (CSA) based model against a least-square support vector machine (LS-SVM) baseline.

Main Methods:

• Utilized the clonal selection algorithm (CSA) to construct a predictive model for SSTP effluent quality.
• Employed the least-square support vector machine (LS-SVM) as a benchmark for performance comparison.
• Validated the models using case study data to assess prediction accuracy and efficiency.

Main Results:

• The CSA-based SSTP model demonstrated satisfactory performance, comparable to the LS-SVM model.
• The CSA approach requires fewer control and training parameters compared to LS-SVM.
• The CSA model proved effective in handling limited data, non-linear functions, and multidimensional pattern recognition.

Conclusions:

• The clonal selection algorithm (CSA) is a powerful and efficient tool for modelling effluent removal prediction in septic sludge treatment plants.
• CSA offers advantages in parameter efficiency and data handling capabilities for environmental modeling.
• The developed CSA model provides a valuable planning tool for SSTP development and management, especially in resource-limited settings.