Digital territories focus on community transformation through sustainable development, saving resources in local governments, bridging the digital gap, and using technology to build smart infrastructure. This article presents the design and implementation of a smart system, called the Waste Treatment System (WTS), for controlling parameters of waste decomposition in the trash bins installed in Riobamba city (Ecuador). The prototype allows monitoring in real time both the amount of waste and the level of rottenness of garbage by means of the measurement of different parameters that indicate the characteristics of the leachates generated inside. The motivation of this work was to yield an efficient solution to urban waste treatment that optimizes resources in the collection process by providing real-time information to improve collection frequency vehicles and also reduce emissions by the decomposition of organic waste. The tests allowed assessing technical aspects such as the maximum coverage of wireless communication, the transmission channel capacity for each prototype, the data-processing requirements, and other more particular parameters such as the production of leachates due to the frequency of collection and the environmental conditions, which will be useful in future work on environmental impact.

This paper proposes a fuzzy-based energy management strategy (EMS) to maximize the self-consumption from a PV installation with an energy storage system (ESS) for the residential sector adapted to the Ecuadorian electricity market. The EMS includes two control levels: Energy management at the end-user level (Fuzzy-based EMS and optimized by genetic Algorithm) and Energy management at the distribution grid level (Fuzzy-based EMS). Both strategies aim to maximize the use of the energy generated at home (taking into account the local solar generation profile), fulfilling the loads’ demand and injecting the energy surplus into the main grid to be economically compensated. Additionally, this paper presents economical modeling according to the electricity market in Ecuador. The main results showed a cost reduction in the electricity bill up to 83.64% from the base case (residential consumption without a PV system). In the scenario of a community electricity market (still not contemplated under the Ecuadorian electricity law), the potential economic savings may be more than double compared to the exact case but only with a self-consumption system.

Wastewater treatment plants consist of many biological reactors and a settler, representing an example of large-scale, nonlinear systems. The wastewater treatment plant in this study operates using an activated sludge system, which relies on biological processes to treat wastewater effectively. It is for this reason that iterative process modeling was used through the implementation of an Extended Kalman Filter (EKF) to predict the height of the sludge layer in secondary clarifiers, where the accumulation of activated sludge occurs during the sedimentation process. This technique consists of maximum likelihood estimation that works more consistently in various noise scenarios. As a result of the evaluation of the model estimated by the Extended Kalman Filter (EKF), the suitability of the process tends to be concluded on. In this sense, the prediction of the height in the sludge layer in sewage systems represents a complicated and heteroscedastic process, which can be understood as a phenomenon that can be influenced by a variety of factors. Therefore, this study does not identify problems in estimates through a thorough examination of residuals. It is concluded that the implementation of state-space modeling increases the adaptability and adjustability of the process to achieve structural optimization in a treatment plant. This approach is a viable and effective solution for the efficient management of polluting sludge levels and minimizing the possible environmental impact in out-of-control situations in wastewater treatment plants.

Conventional fuzzy multi-criteria decision-making (MCDM) methods support ranking under uncertainty but often provide limited explanation of why alternatives are preferred. This study proposes an explainable fuzzy decision-making framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy TOPSIS with surrogate modeling, SHAP-based analysis, and linguistic rule extraction. The main contribution is an explanation layer that preserves the original FAHP–FTOPSIS ranking structure while decomposing ranking scores into criterion-level contributions and transforming recurrent attribution patterns into IF–THEN rules. The framework is evaluated through a supplier-selection case study using expert fuzzy evaluations, local perturbation analysis, leave-one-supplier-out cross-validation, and a synthetic benchmark. The results show that the fuzzy MCDM layer produces discriminative rankings and that the top-ranked supplier remains comparatively stable under perturbations. Among the tested surrogates, the Random Forest Regressor achieved the strongest local fidelity, outperforming linear regression and a shallow decision tree. SHAP analysis showed ordinal alignment between FAHP weights and global criterion importance, while the extracted rules achieved high coverage, consistency, and threshold stability. The framework is useful for researchers, decision analysts, procurement managers, and supply chain professionals who require transparent, interpretable, and auditable multicriteria decisions under uncertainty.