# A Neural Network Model for Decision-Making with Application in Sewage Sludge Management
## 3 Materials and Methods
\begin{table}
\begin{tabular}{c c c c} \hline
**Phase** & **Treatment** & **Strengths** & **Limitations** \\ \hline Phase 1 & & & Water treatment interruption due to sludge removal. \\ (Th\({}_{\text{i}}\)) & & & \\ \hline \multirow{4}{*}{Gravity thickening (Th\({}_{\text{2}}\))} & Moderate investment and & & Long attention times. \\  & & management costs. & It captures only \\  & & Simple management of the process. & sedimentable solids. \\  & & Storage capacity. & \\ \hline \multirow{4}{*}{Dissolved air flotation (Th\({}_{\text{3}}\))} & Reduced detection times. & & \\  & Good reduction of & & \\  & & suspended substances. & \\ \hline \multirow{4}{*}{Gravity thickening for primary sludge and gravity belt thickening for secondary sludge (Th\({}_{\text{4}}\))} & The best treatment for both primary and secondary sludge. & & \\  & & Good solids content in the sludge. & Hard management. \\ \hline \multirow{4}{*}{Dissolved air flotation (Th\({}_{\text{3}}\))} & Reduced detection times. & & \\  & Good reduction of & & \\  & & suspended substances. & \\ \hline \multirow{4}{*}{Gravity thickening for primary sludge and gravity belt thickening for secondary sludge (Th\({}_{\text{4}}\))} & The best treatment for both primary and secondary sludge. & & \\  & Good solids content in the sludge. & & Hard management. \\ \cline{1-1}  & & Storage capacity. & \\ \hline \end{tabular}
\end{table} 
Table 2: Strengths and limitations of all considered alternatives to carry out the thickening treatments of the modeled SSTS. \begin{table}
\begin{tabular}{c c c} \hline
**Phase** & **Treatment** & **Strengths** & **Limitations** \\ \hline Phase 2 & & \\ Stabilization (Sb\({}_{\text{1}}\)) & Simple management. & \\ Stabilization (Sb\({}_{\text{1}}\)) & Low sensitivity to toxic discharges. & \\ (Sb\({}_{\text{i}}\)) & & Good removal of heavy metals. & \\ \hline \multirow{4}{*}{Aerobic digestion (Sb\({}_{\text{2}}\))} & Low investment and management costs. & \\  & & Low sensitivity to external factors. & \\  & & Low sensitivity to toxic discharges. & \\  & & No production of bad odors. & \\ \hline \multirow{4}{*}{Anaerobic digestion (Sb\({}_{\text{3}}\))} & Intake of oxygen from the external environment is not required. & \\  & & Low energy consumption. & \\ \cline{1-1}  & & Low production of stabilized sludge. & \\ \cline{1-1}  & & High reduction of the pathogens in the sludge. & \\ \cline{1-1}  & & Energy recovery. & \\ \hline \multirow{4}{*}{Anaerobic digestion for primary sludge and aerobic digestion for secondary sludge (Sb\({}_{\text{4}}\))} & Lower energy costs. & \\ \cline{1-1}  & Only primary sludge is subjected to anaerobic digestion. & \\ \hline \end{tabular}
\end{table} 
Table 3: Strengths and limitations of all considered alternatives to carry out the stabilization treatments of the modeled SSTS. compositing (Co) and thermal drying (Td) (i.e., Phases 4 and 5) of the SS treatment. The strengths and limitations of each alternative considered for dewatering, compositing, and thermal drying are summarized in Tables 4 and 5, respectively. The ANN model provides information about the necessity to perform (yes) or not perform (no) the operations included in these phases. Finally, a set of three alternative operations was evaluated for the disposal of the treated sludges; the firsts two alternatives consist of spreading of the not-dewatered (Dp\({}_{1}\)) or dewatered (Dp\({}_{2}\)) sludge on the agricultural land. The third possible alternative, evaluated by the ANN model, consists of the incineration of the treated SS (Table 6). The input parameters of the ANN model are plant capacity (PC), typology of secondary treatment (St) of the WWT, and the configuration of the SS treatment plant (CSp).