# A Neural Network Model for Decision-Making with Application in Sewage Sludge Management
## 2 Literature Review
Similarly, [PERSON] et al. developed an LCA to evaluate the environmental performance of SS incineration and to provide information for improving the operating conditions of the treatment from an environmental sustainability perspective [15]. [PERSON] et al. applied the LCA methodology to evaluate the environmental impact of the technological parameters related to the SS treatments, considering the thickening treatment, the anaerobic digestion with the cogeneration of heat and electricity, the mechanical dewatering, and the spreading of the SS on agricultural land. They found that the environmental performance of SS treatment could be improved by increasing biogas production by reducing FeCl\({}_{3}\) consumption for prior treatment to dewatering and by identifying alternatives for handling SS return liquors [16]. Concerning DSS in SSs management (i.e., third cluster), recently a process data analytics platform for adopting the soft sensors in waste-to-energy (WTE) plant was developed. The platform uses machine learning methods coupled with big-data processing tools and cloud computing technologies. The platform's application allowed them to monitor the process parameters to maximize the performance of the WTE plant [17]. A similar approach was adopted for the management of the healthcare waste disposal system. In this context, a decision-making trial and an evaluation laboratory (DEMATEL) method were developed to digitally connected healthcare centers, waste disposal firms, and pollution control board. In this way, efficient monitoring of the entire waste supply chain improves the performance under the environmental perspective [18]. A comprehensive decision  
Figure 2: Authors’ keywords co-occurrence network map. model, including technical, environmental, economic, and social factors for the evaluation of SS treatment strategies, was proposed by Reference [19]. The model was tested on a case study of 500,000 inhabitants; the authors proved the model's significant added value if it is properly employed. Consistent with this aspect, the main limitations of the decision model introduced were related to the user's strong dependency. In other words, the user plays a fundamental role in phases such as the data-collection process and result interpretation; therefore, inexperienced users could yield meaningless outcomes even if using the tool in a formally correct way. [PERSON] et al. applied a Logarithmic Fuzzy Preference Programming based Fuzzy Analytic Hierarchy Process (LFPPFAHP) and Extension theory to assess the sustainability of three ways of SS management (i.e., composing, incineration, and resource utilization). As a result, composting was defined as \"Moderately Sustainable\", incineration as \"Not Sustainable\", and resource utilization as \"highly sustainable\" [20]. The adoption of SS for energy production was faced in 2018, by [PERSON] et al., in the study conducted; an ANN model was employed to predict the thermal decomposition of high-ash sewage sludge. The results achieved showed a good agreement between the experimental values and predicted values [21]. The same method was adopted to predict the daily sewage sludge quantity in WWTP; in this case, different ANN architecture was evaluated to increase the prediction reliability. The minimum value of the root mean square error (RMSE) and mean absolute error (MAE) was identified by adopting the six mother wavelet (W) functions as preprocessor [22]. An ANN trained with back-propagation (BP) algorithm and a generalized regression neural network model (GRNN) were compared to predict the thin-layer drying behavior in municipal sewage sludge during hot-air forced convection. The research proved a better performance of the BP model to predict the moisture content of the sludge thin layer than the GRNN model [23]. The papers considered can be classified according to dimension evaluated (i.e., technological and economic), features of the method adopted, and possible application of multiple-criteria decision-making (MCDM).