A Neural Network Model for Decision-Making with Application in Sewage Sludge Management

Abstract

Wastewater treatment (WWT) is a foremost challenge for maintaining the health of ecosystems and human beings; the waste products of the water-treatment process can be a problem or an opportunity. The sewage sludge (SS) produced during sewage treatment can be considered a waste to be disposed of in a landfill or as a source for obtaining raw material to be used as a fertilizer, building material, or alternative fuel source suitable for co-incineration in a high-temperature furnace. To this concern, this study’s purpose consisted of developing a decision model, supported by an Artificial Neural Network (ANN model), allowing us to identify the most effective sludge management strategy in economic terms. Consistent with the aim of the work, the suitable SS treatment was identified, selecting for each phase of the SS treatment, an alternative available on the market ensuring energy and/or matter recovery, in line with the circular water value chain. Results show that the ANN model identifies the suitable SS treatments on multiple factors, thus supporting the decision-making and identifying the solution as per user requirements.

Assessing circular economy in Brazilian industries through the analytical hierarchy process

“…The descriptors used were: (ALL ("circular economy" ) AND ( "Measurement" ) AND ( "Sustainable Development" ) AND ( "Analytical Hierarchy Process" ) ) AND ( ( ( circular AND economy ) ) AND ( measurement ) ) AND ( sustainable ) AND ( LIMIT-TO ( PUBYEAR , 2022 ) OR LIMIT-TO ( PUBYEAR , 2021 ) OR LIMIT-TO ( PUBYEAR , 2020 ) OR LIMIT-TO ( PUB-YEAR , 2019 ) OR LIMIT-TO ( PUBYEAR , 2018 ) ) AND ( LIM-IT-TO ( DOCTYPE , "ar" ) ). The state-of-the-art review revealed 148 possible articles, 28 of which were good matches for the subject and were used for the CE discussions since they addressed some of the CE steps, such as, for example, reduce, reuse, and recycle (Kumar et al, 2021;, or focused on energy and carbon emissions (Mishra et al, 2021); solar energy (Erol et al, 2021), biomass (Facchini et al, 2021), and e-waste (Alblooshi et al, 2022). However, most of these publications refer to the need to identify barriers to CE implementation (Kumar et al, 2021;Ullah et al, 2021;Irfan et al, 2022).…”

Special Issue on Sewage Sludge: Latest Advances and Prospects

“…Facchini et al [5] proposed a decision model to identify the most effective sludge management strategy in economic terms identifying the suitable sewage sludge treatment based on multiple factors.…”

Using LSTM Neural Network Based on Improved PSO and Attention Mechanism for Predicting the Effluent COD in a Wastewater Treatment Plant

“…Bakr et al established an ANN model optimized based on the Levenberg-Marquardt algorithm to simulate the performance of auto-aerated immobilized biomass (AIB) reactor packed with sponge media, experiment showed that the R 2 (the coefficient of determination) value of the model was satisfactorily fit in training, verification and testing and that the model can reflect reality [15]. In [16], Facchini et al developed a decision model supported by an ANN model to determine an economic sludge management strategy and experimentally showed that the model can identify appropriate sludge treatment options based on multiple characteristics, thus supporting decision-making. Bekkari et al used the ANN model to predict the effluent COD of the Touggourt WWTP, and the results indicated that the ANN modeling approach can provide an effective tool for simulating, controlling and predicting the performance of WWTPs [17].…”