Abstract
Water scarcity is currently a concerning problem and is likely to worsen in the future. To address this issue, it is essential that water used in human activities is treated before being reused or returned to nature. Wastewater is processed in wastewater treatment plants (WWTPs), which are complex structures that consume a considerable amount of resources and need to operate optimally. Many authors have proposed computational methodologies to optimize WWTPs, and each work has different approaches and characteristics, but most have in common the lack of concern with maximizing sustainability, in its broadest definition. Furthermore, even when sustainability is considered, it is typically addressed in an indirect or superficial manner, rather than being treated as a central objective. This paper provides a critical literature review of computational methodologies that, in some way, focus on improving the sustainability of WWTPs. Considering the target of the paper, this review aims to answer the following main questions: (1) What are the general objectives of the proposed works? (2) In which locations/phases of the treatment process are the proposed techniques applied? (3) What are the main methodologies and performance metrics used in the proposed techniques? The review identifies a strong focus on optimizing aeration in biological reactors, limited holistic and real-time optimization across WWTP stages, and sparse integration of sustainability metrics, especially for environmental and social impacts. Future research should prioritize the development of real-time, multi-objective optimization frameworks that encompass all WWTP stages and fully integrate economic, environmental, and social sustainability dimensions.

Abstract

Abstract
The evaluation of industrial process efficiency is essential for resource optimization, enabling the identification of bottlenecks, waste, and improvement opportunities while promoting the rational use of resources and enhancing the sustainability of operations. The Multi-layer Stream Mapping (MSM) method is a tool for assessing the efficiency of complex production processes, which identifies the efficiencies and inefficiencies based on reference values. However, its limitation lies in using reference values that often fail to reflect process evolution or distinct operational regimes. This work proposes a new dynamic ecoefficiency assessment methodology, the Evolving Fuzzy Multilayer Stream Mapping System (eFuMSM), based on MSM and an evolving fuzzy system, to provide dynamic reference values, allowing more accurate eco-efficiency assessments considering the process evolution and different regions of operation. The proposed eFuMSM was applied to the primary clarifier of a wastewater treatment plant, evaluating efficiency in removing total suspended solids. Results revealed that systems previously undervalued under the traditional MSM demonstrated improved efficiency when assessed using the eFuMSM system, aligning more accurately with their operational regimes.

Abstract
This paper aims to clarify the characteristics of Digital Twins (DTs) in their most advanced conceptual development, Cognitive Digital Twins (CDTs), and analyze their support for the implementation of the Circular Economy (CE). A systematic literature review was conducted using a specially developed five-dimensional analytical framework to characterize DT proposals and their potential for CE based on an established framework for circularity strategies. The study indicates that cognitive and hybrid DT approaches tend to cover high levels of interoperability, data flow, system levels, and cognitive processes. However, CDT use in CE demands harmonizing different strategies to cover the complete product lifecycle, which recent research on DTs has not fully addressed. This study is the first to systematically review cognitive digital twins and their relation to circularity, offering an analytical framework that can be expanded for future research in various application areas of Industry 5.0.