Featured publication in Applied Energy: Using AI to Unlock Energy Savings from Waste Heat in Water Treatment
How a data-driven approach to energy recovery maximizes efficiency in wastewater treatment.
Researchers are exploring new ways to improve energy efficiency in wastewater treatment plants by capturing and converting waste heat into electricity. Using a data-driven model and economic analysis, the study evaluates the feasibility and potential impact of integrating Organic Rankine Cycle (ORC) systems and seasonal thermal energy storage (STES) to recover otherwise lost energy.
Scientific Achievement
A machine learning model based on artificial neural networks (ANN) was developed to predict waste heat recovery (WHR) using operational data such as biogas temperature, pressure, and daily energy production. The model enables precise forecasting of heat recovery potential at treatment plants.
Economic Impact
Turning waste heat into electricity can support cleaner, more energy-abundant water systems. The analysis estimates $5.28 million in potential savings over five years through prototype WHR implementation. However, coupling ORC with STES increases system costs, raises the Levelized Cost of Energy (LCOE) to $0.0824/kWh, and is currently considered economically infeasible.
Why It Matters
Water treatment is energy intensive and therefore costly. Applying advanced tools like AI and waste heat recovery technologies can help reduce operational costs, contributing to more sustainable infrastructure worldwide.
Abstract
Maximizing energy efficiency through waste heat recovery (WHR) processes is crucial for sustainable and eco-friendly operations across multiple industries, notably in wastewater treatment plants (WWTPs).
This work proposes a comprehensive approach for assessing the WHR feasibility in WWTPs, structured in two main objectives. Firstly, an Artificial Neural Network (ANN) model is developed to accurately predict WHR based on operational data, including biogas temperature, biogas pressure, daily production in kWh, and WHR values in kWhth.
The second objective focuses on economically evaluating the WHR feasibility based on the estimated WHR values obtained by the ANN model, and then realistically assessing the economic feasibility of integrating the Organic Rankine Cycle (ORC) and Seasonal Thermal Energy Storage (STES) systems.
The process if implemented commercially can greatly reduce the environmental burden of batteries as the greenhouse gas emissions of 8.25 kg CO2e kg−1 from the direct recycling process are 64% lower compared to those from virgin production of cathode material.
With an application to the As-Samra WWTP located in Jordan, the developed ANN model demonstrates promising results in the validation phase, with a root mean square error (RMSE) of 2206 kWh/day, a mean absolute error (MAE) of 1674 kWh/day, and an R-squared (R2) value of 68%.
On the other hand, the economic analysis reveals that an optimal ORC system of 412.14 kWe capacity yields a Net Present Value (NPV) of 2.09 million US dollars, a Levelized Cost of Energy (LCOE) of 0.0749 USD/kWh, a Payback Period (PBP) of 4.8 years, and annual revenues of 428 kUSD. This work also investigates the techno-economic feasibility of integrating ORC-STES.
Results indicate that the LCOE and PBP are highly affected by the ORC’s capital cost, and integrating STES increases the LCOE to 0.0824 USD/kWh, rendering its integration with ORC infeasible. This study aims to advance the understanding and application of WHR in WWTPs, paving the way for more efficient and sustainable practices in the field.