# A Neural Network Model for Decision-Making with Application in Sewage Sludge Management
## 5 Conclusions  
The work conducted consisted of developing an ANN model that aims to identify the suitable SS treatment, based on a framework consisting of six steps and a predefined series of alternatives for each step, to suggest a most effective sludge-management strategy in economic terms. The developed ANN model was tested on 20 possible scenarios, identified considering out-of-sample data. The results showed the ANN model's effectiveness in supporting the decision-makers in identifying the SS treatment to be adopted. As it can be observed in Table 9, indeed, the application of the ANN to the 20 scenarios considered presents an average ME of 0.131 and a MAPE of 14.88%. The alternatives considered for each phase of the SS treatment are sustainable in economic terms. They are consistent with a circular economy approach, where the SS should be considered a resource more than a waste. The cases extracted from the literature review and the experts' opinion collected constituted a solid dataset to train the ANN to provide, by varying the input parameters, the most effective sludge management strategy in economic terms. Most of alternatives considered by ANN model allow for energy and/or matter recovery in line with the circular water value chain. The tool allowing the implementation of the ANN model is user friendly and is free. The approach adopted lead to standardizing the decision-making process of the management of the SS. The management of which is generally considered too complex and with conflicting objectives and interests. However, the ANN model requires a validation test on a full-real case study, as well as an upgrading of the possible alternatives included for each phase. Consistently whit this improvement, identifying the specific key performance indicators (KPIs) would quantify the performance of the solutions proposed for each scenario, under an economic and environmental perspective. The results showed a good starting point to simplify the issues due to decision problems in this research field. In this context, the Industry 4.0 technologies could support the ANN model, providing a sensors network that improves the communication between stakeholders and plant manager to have continuous monitoring of the process status and by promoting a dynamical decision-making approach. In this case, the information on the transport and storage phases should be considered, with the aim to provide an exhaustive picture of the WWTP such as to ensure a minimal impact on the entire water value chain. Considering the technical issues related to ANN model implementation, an improvement of the current works could consist of integrating VBA with a database in HTML5; thus, multiple applications could be used to support the ANN model. Moreover, in this study are not considered aspects according to the concept of Triple Bottom Line (TBL), meaning the balance of economic, environmental, and social factors in corporate decision-making [29]. This represents a limit that could be challenged in future studies. Similarly, more alternatives for each phase concerning disposal should be considered to improve the effectiveness of the strategies proposed by the model. Conceptualization, F.F. and M.V. ; methodology, M.V. ; software, F.F. ; validation, F.F., M.V. and L.R. ; formal analysis, F.F. ; investigation, M.V. ; writing--original draft preparation, M.V. ; writing--review and editing, F.F. ; visualization, L.R. ; supervision, F.F. ; funding acquisition, F.F. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by the AIM project (Attraction and International Mobility) funded by the Italian Ministry of Education, Universities and Research MIUR, grant number AIM1888385. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest.