ENVIRONMENTAL FOOTPRINT REDUCTION FOR WASTEWATER TREATMENT BY MULTI-OBJECTIVE OPTIMIZATION AND MODELLING

The current challenge faced by researchers and managers is minimize the environmental footprint of wastewater treatment plants (WWTPs). To accomplish this goal, other than respect legal requirements, WWTPs must provide better responses regarding consumption of energy and resources, greenhouse gas (GHG) emissions, among others. The research hereby presented is based on the application of mathematical modelling with the aim to propose a framework for the minimization of WWTP’s environmental footprint towards optimization techniques. A literature review was held to gain knowledge regarding the main decision support systems (DSS) that are applied to WWTPs since they allow selecting the most appropriate solutions regarding the plant’s performance. The DSSs were also investigated to understand their main uses while applied to WWTPs. It has been found that mathematical models (MMs) are very often applied to the wastewater treatment, especially regarding conventional activated sludge (CAS) and membrane bioreactors (MBRs). It was also noted that none of the works applied MMs with a multi-objective purpose towards minimizing their environmental footprint. On this behalf, two stationary process-based mathematical models were developed in order to carry out a comprehensive comparison between CAS (Model I) and MBR (Model II) in terms of GHG emissions and energy consumption. Model I was applied to a full-scale real plant located in the city of Irvine, in California, while Model II was applied to a semi-hypothetical case study obtained by replacing the secondary settler of the CAS with a membrane bioreactor. Results showed that the MBR demanded more energy than the CAS due to the aeration required by the activated sludge process (ASP). MBRs were also found to be responsible for higher indirect emissions. Following the previous results, a focus was given to the ASP of MBRs. Since MBR’s ASP is mainly composed by the integration of biological and physical treatments, a literature review was held to gain knowledge regarding the integrated MBR modelling. Results led to the proposition of a framework towards the optimization of MBR’s environmental footprint. Additionally, some opportunities were revealed regarding MBR modelling, leading to the update of an integrated MBR dynamic model, which was applied to an University of Cape Town (UCT) MBR pilot-plant. The new model was able to assess the plant’s performance in terms of ten performance indicators (PIs) related to effluent quality (EQI), operating costs (OC), energy demand (eD), and GHG emissions. Simulations were held to understand the influence of five operational parameters over the ten PIs (benchmark scenario). The method Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was adopted for the optimization of modelling results (optimal scenario). A comparison between the benchmark and optimal scenarios showed an amelioration in the plant’s performance in terms of GHG direct and indirect emissions, energy consumption and operational costs at the expenses of a negligible decay of effluent quality. An optimization technique based on instrumentation, automation and control (ICA) principles was applied to the same UCT-MBR. The approach considered real-time variations of oxygen and ammonia concentrations within the aerated compartments to propose a disturbance-based optimization also in terms of EQI, OC, eD and GHG. The comparison between benchmark and ICA scenarios showed a reduction of 31%, 38%, and 31% in terms of eD, indirect emissions, and OC, respectively, for the ICA scenario. Finally, a user-friendly software-based model was applied to an integrated fixed-film activated sludge (IFAS) MBR pilot plant with the aim to optimize its performance in terms of EQI, eD, OC and GHG by the control of operational parameters. Results retrieved from this work showed great potentiality, but further scenarios should be encouraged to understand the minimum and maximum threshold for C/N ratios so plant can maintain positive results, which may encourage further research. Additionally the software-based approach allowed personalizing a comprehensive dynamic mathematical model in a small amount of time, without recurring to extensive modelling tools based on a programming language. The framework proposed by this research is a novelty in the literature since no studies were previously published with the aim to provide an assessment of advanced treatments in terms of EQI, eD, OC and GHG. Such a comprehensive assessment demonstrated the importance of a model-based DSS to obtain the best trade-off during performance optimization. Finally, the successful application to five different case studies demonstrates that the framework is trustworthy and that can help decision-makers in finding the best trade-off between accuracy and complexity while looking for predictive answers. The current challenge faced by researchers and managers is minimize the environmental footprint of wastewater treatment plants (WWTPs). To accomplish this goal, other than respect legal requirements, WWTPs must provide better responses regarding consumption of energy and resources, greenhouse gas (GHG) emissions, among others. The research hereby presented is based on the application of mathematical modelling with the aim to propose a framework for the minimization of WWTP’s environmental footprint towards optimization techniques. A literature review was held to gain knowledge regarding the main decision support systems (DSS) that are applied to WWTPs since they allow selecting the most appropriate solutions regarding the plant’s performance. The DSSs were also investigated to understand their main uses while applied to WWTPs. It has been found that mathematical models (MMs) are very often applied to the wastewater treatment, especially regarding conventional activated sludge (CAS) and membrane bioreactors (MBRs). It was also noted that none of the works applied MMs with a multi-objective purpose towards minimizing their environmental footprint. On this behalf, two stationary process-based mathematical models were developed in order to carry out a comprehensive comparison between CAS (Model I) and MBR (Model II) in terms of GHG emissions and energy consumption. Model I was applied to a full-scale real plant located in the city of Irvine, in California, while Model II was applied to a semi-hypothetical case study obtained by replacing the secondary settler of the CAS with a membrane bioreactor. Results showed that the MBR demanded more energy than the CAS due to the aeration required by the activated sludge process (ASP). MBRs were also found to be responsible for higher indirect emissions. Following the previous results, a focus was given to the ASP of MBRs. Since MBR’s ASP is mainly composed by the integration of biological and physical treatments, a literature review was held to gain knowledge regarding the integrated MBR modelling. Results led to the proposition of a framework towards the optimization of MBR’s environmental footprint. Additionally, some opportunities were revealed regarding MBR modelling, leading to the update of an integrated MBR dynamic model, which was applied to an University of Cape Town (UCT) MBR pilot-plant. The new model was able to assess the plant’s performance in terms of ten performance indicators (PIs) related to effluent quality (EQI), operating costs (OC), energy demand (eD), and GHG emissions. Simulations were held to understand the influence of five operational parameters over the ten PIs (benchmark scenario). The method Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was adopted for the optimization of modelling results (optimal scenario). A comparison between the benchmark and optimal scenarios showed an amelioration in the plant’s performance in terms of GHG direct and indirect emissions, energy consumption and operational costs at the expenses of a negligible decay of effluent quality. An optimization technique based on instrumentation, automation and control (ICA) principles was applied to the same UCT-MBR. The approach considered real-time variations of oxygen and ammonia concentrations within the aerated compartments to propose a disturbance-based optimization also in terms of EQI, OC, eD and GHG. The comparison between benchmark and ICA scenarios showed a reduction of 31%, 38%, and 31% in terms of eD, indirect emissions, and OC, respectively, for the ICA scenario. Finally, a user-friendly software-based model was applied to an integrated fixed-film activated sludge (IFAS) MBR pilot plant with the aim to optimize its performance in terms of EQI, eD, OC and GHG by the control of operational parameters. Results retrieved from this work showed great potentiality, but further scenarios should be encouraged to understand the minimum and maximum threshold for C/N ratios so plant can maintain positive results, which may encourage further research. Additionally the software-based approach allowed personalizing a comprehensive dynamic mathematical model in a small amount of time, without recurring to extensive modelling tools based on a programming language. The framework proposed by this research is a novelty in the literature since no studies were previously published with the aim to provide an assessment of advanced treatments in terms of EQI, eD, OC and GHG. Such a comprehensive assessment demonstrated the importance of a model-based DSS to obtain the best trade-off during performance optimization. Finally, the successful application to five different case studies demonstrates that the framework is trustworthy and that can help decision-makers in finding the best trade-off between accuracy and complexity while looking for predictive answers.