Multi-objective energy consumption optimization in a pumping station using a hybrid model based on the grey wolf algorithm and vector regression.

Wastewater treatment plants (WWTPs) play a vital role in protecting public health and the environment, but the treatment process itself is energy-intensive. Among the various stages, pumping stations stand out as major energy consumers, responsible for lifting wastewater through the treatment cycle. Traditionally, these stations have relied on fixed-speed pumps, leading to inefficiencies when dealing with variable inflow rates – a common occurrence in WWTPs. Recent advancements in artificial intelligence (AI) offer promising solutions for optimizing pump control strategies and minimizing energy consumption. AI techniques like machine learning and optimization algorithms can analyze real-time data, predict future inflow trends, and dynamically adjust pump speeds accordingly. Studies have demonstrated the effectiveness of AI-based pump control in reducing energy use by significant margins. This research explores a novel AI-based approach combining the Grey Wolf Optimization (GWO) algorithm and Support Vector Regression (SVR) for optimizing pump speed control in WWTP pumping stations. GWO, inspired by grey wolf hunting behavior, identifies optimal pump operation parameters to minimize energy consumption. SVR, a machine learning technique, predicts future pump speed requirements based on real-time data and the optimized GWO parameters. Utilizing real-world data from Zahedan Refinery's pumping station, this study aims to validate the proposed GWO-SVR approach and assess its potential for reducing energy consumption in WWTPs, thereby enhancing operational efficiency and sustainability.In this section, an optimization model for a wastewater pump is developed and presented. The pump is situated in a reservoir where the inflow pattern is randomly generated based on a real-world measured dataset. The Zahedan Wastewater Treatment Plant is located on a 24-hectare land in the east of the city, designed to serve a population of about 900,000 people in the future. The project includes 17 kilometers of transmission lines, a reservoir, and a pumping station. The hourly inflow data to the treatment plant was measured for a pumping station with a constant cross-sectional area of the reservoir of 12 square meters, and a minimum and maximum height of 1 and 9 meters, respectively, in the year 1400 (2021). Based on the 12 month data of 1400, the maximum average wastewater flow rate was 6.99 liters per second in June, and the maximum standard deviation was 68.27 in February (Table 1)The optimization problem is formulated to minimize daily energy consumption for pumping equation(12).Two models are compared:• Classic Model: Fixed pump speed operation• Optimization Model: GWO-based variable speed control for optimized energy usageSVR is introduced to predict pump speed based on data obtained from the optimization model.Discussion and The GWO algorithm is implemented to identify optimal pump operation parameters (Table 3). The SVR model is then employed to predict pump speed based on these optimized parameters. The optimized model is compared to a fixed-speed system, demonstrating significant energy savings. Figures 4 to 6 show the changes between constant-speed (ηcs) and variable-speed (ηopt) performance based on α and β for different time windows of Zahedan station in 2021. With increasing inflow rates (decreasing α), the variable-speed pump performance improves over the constant-speed pump, widening the gap between ηopt and ηcs. Conversely, with decreasing inflow rates (increasing α), this gap narrows. The parameter β, which regulates pump speed, increases performance for both pump types when raised. These optimizations were performed for 30, 60, and 120 minute time windows in 2021, with the variable-speed pump outperforming the constant-speed pump in all cases. Across all time windows, increasing the inflow rate (smaller α) results in a larger energy saving (ε) for the variable-speed pump over the constant-speed pump. With increasing β, which allows higher pump speeds, ε decreases for the same inflow rates (constant α). This is because at higher β values, the variable-speed pump behaves more like a constant-speed pump. The correlation coefficients comparing these optimization results to simulations in 2021 are 0.9996-0.9999 for pump speed and 0.9976-0.9999 for energy, indicating the high accuracy of the SVR simulations and GWO optimization. Figure 10 compares the optimized GWO and simulated SVR results for pump speed in a 60-minute window, while Figure 11 shows the same for pump energy consumption. The close agreement between the optimization and simulation demonstrates the effectiveness of the proposed GWO-SVR model. This study investigated a novel AI-based approach combining the Grey Wolf Optimization (GWO) algorithm and Support Vector Regression (SVR) model for optimizing pump speed control in wastewater treatment plant pumping stations. The GWO-SVR model demonstrated significant energy savings in the variable-flow pumping station compared to a fixed-speed system. The SVR model accurately predicted optimal pump speeds based on real-time data and GWO-optimized parameters, enabling efficient, dynamic pump control. The combined GWO-SVR approach provides a practical solution for WWTP operators to optimize pump control, achieve substantial energy savings, and contribute to more sustainable and cost-effective wastewater treatment practices. Its successful implementation in existing pumping stations can drive improved operational efficiency and environmental sustainability.Keywords: Energy efficiency - simulation - pumping station - gray wolf algorithm - support vector regression