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The proposed research work is focused on forecasting the future requirements of water supply based on the current requirement of water and also identifying the possibility of occurrences of cracks and leaks using the ARIMA (autoregressive integrated moving average) model. The experiments were conducted using real-time experimental hardware. The pressure data obtained and their p -value is less than 0.05, which represents the stability of the data in the ARIMA model. The forecasted pressure data range between 0.451379 N/m2 and 2.022273 N/m2. The frequency of the forecasted pressure ranges between 1.706869 N/m2 and 3.065836 N/m2 (maximum peak) and −0.81046 N/m2 and 1.042164 N/m2 (minimum peak). Forecasted data of pressure at damaged condition lie between 2.880788 N/m2 and 3.29797 N/m2 and frequency ranges between 4.866227 N/m2 and 5.664348 N/m2. Similarly, future forecasted data of water requirement for the next 1 year range between 614.6292 (liters/week) and 620.0099 (liters/week), the frequency of the forecast value with maximum ranging from 617.0086 (liters/week) to 628.5465 (liters/week), and the minimum peaks ranging from 611.0967 (liters/week) to 612.2914 (liters/week). The above data are for a single water distribution pipeline.

The feasibility of the Non-thermal Plasma (NTP) process is examined by four operating parameters including NOx concentration (300-400 ppm), gas flow rate (2-6 lpm), voltage (20-30 kV) and electrode gap (3-5 mm) using a Dielectric Barrier Discharge (DBD) reactor for removing NOx from diesel engine exhaust. Based on the NTP study, the NOx removal efficiency and energy efficiency of the NTP reactor are measured. Optimization of process parameters have been carried out using response surface-based Box Behnken Design (BBD) method and Artificial Neural Network (ANN) method. ANN based optimization is carried out using feed-forward network algorithm which has 4 input nodes, 10 hidden nodes and 2 output nodes. Based on the RSM and ANN model study, R2 value are obtained as 0.98 and 0.99 respectively. These models demonstrates that they have strong agreement with the experimental results. The results are indicated that the RSM model's optimum conditions resulted in a maximum NOx reduction of 60.5% and an energy efficiency of 66.24 g/J. The comparison between the two models confirmed the findings, whereas this ANN model displayed a stronger correlation to the experimental evidence.