Sustainability ranking of desalination plants using Mamdani Fuzzy Logic Inference Systems

As water desalination continues to expand globally, desalination plants are continually under pressure to meet the requirements of sustainable development. However, the majority of desalination sustainability research has focused on new desalination projects, with limited research on sustainability performance of existing desalination plants. This is particularly important while considering countries with limited resources for freshwater such as the United Arab Emirates (UAE) as it is heavily reliant on existing desalination infrastructure. In this regard, the current research deals with the sustainability analysis of desalination processes using a generic sustainability ranking framework based on Mamdani Fuzzy Logic Inference Systems. The fuzzy-based models were validated using data from two typical desalination plants in the UAE. The promising results obtained from the fuzzy ranking framework suggest this more in-depth sustainability analysis should be beneficial due to its flexibility and adaptability in meeting the requirements of desalination sustainability

FUZZY MAMDANI EXPERT SYSTEM FOR PROPERNESS OF SELECTING SUPPLIER IN CORPORATE XYZ

This research aims to enhance the supplier selection process at Corporate XYZ by implementing the Fuzzy Mamdani Expert System, which addresses the complexities of evaluating suppliers in a service-oriented business environment. The study employs a fuzzy logic approach to assess suppliers based on multiple qualitative and quantitative criteria, including cost efficiency, reliability, and scalability. The methodology involves fuzzification of input data, rule base evaluation, and the application of the Mamdani inference system to derive crisp scores for each supplier. The findings indicate that Supplier A scored 85 points, outperforming Supplier B, which scored 70 points, highlighting the effectiveness of the evaluation process. Additionally, the research identifies potential risks associated with suppliers, such as pending legal documentation, which could impact their overall scores. The conclusion emphasizes that the Fuzzy Mamdani Expert System not only facilitates informed decision-making in supplier selection but also fosters continuous improvement through a feedback loop mechanism. This study contributes to the field of supply chain management by demonstrating the applicability of fuzzy logic in optimizing supplier evaluations, ultimately leading to better supplier relationships and cost efficiencies for organizations. Future research is suggested to explore the integration of additional criteria and advanced analytical techniques

A DMAIC integrated fuzzy FMEA model: A case study in the automotive industry

The growing competitiveness in the automotive industry and the strict standards to which it is subject, require high quality standards. For this, quality tools such as the failure mode and effects analysis (FMEA) are applied to quantify the risk of potential failure modes. However, for qualitative defects with subjectivity and associated uncertainty, and the lack of specialized technicians, it revealed the inefficiency of the visual inspection process, as well as the limitations of the FMEA that is applied to it. The fuzzy set theory allows dealing with the uncertainty and subjectivity of linguistic terms and, together with the expert systems, allows modeling of the knowledge involved in tasks that require human expertise. In response to the limitations of FMEA, a fuzzy FMEA system was proposed. Integrated in the design, measure, analyze, improve and control (DMAIC) cycle, the proposed system allows the representation of expert knowledge and improves the analysis of subjective failures, hardly detected by visual inspection, compared to FMEA. The fuzzy FMEA system was tested in a real case study at an industrial manufacturing unit. The identified potential failure modes were analyzed and a fuzzy risk priority number (RPN) resulted, which was compared with the classic RPN. The main results revealed several differences between both. The main differences between fuzzy FMEA and classical FMEA come from the non-linear relationship between the variables and in the attribution of an RPN classification that assigns linguistic terms to the results, thus allowing a strengthening of the decision-making regarding the mitigation actions of the most “important” failure modes.publishersversionpublishe

Fuzzy Logic Harmony in Water: Mamdani Inference System Applied to Evaluate Pristine Pond Water Quality

Aquatic ecosystems that are subject to urbanization and environmental changes, such as the Kapaleeswarar and Chitrakulam tanks, depend on evaluating water quality. Their complicated data present challenges for conventional approaches. The usefulness of the Mamdani fuzzy inference system in determining the water quality in these tanks is investigated in this work. It creates a comprehensive assessment based on subject-matter expertise by handling ambiguous descriptors with linguistic variables and fuzzy sets. The system’s procedures for implementation are described in detail, with an emphasis on how well they can manage interrelated variables. The study shows how well the system measures the water quality in tanks and suggests ways to improve it. Tank evaluation that incorporates the Mamdani system encourages comprehensive resource management and cultural preservation

Bibliometric review of research on decision models in uncertainty

Societies experience intense and frequent changes in diverse environments, which increase uncertainty and complexity in decision-making. The decision-maker looks for alternatives to reduce risks and face these new challenges. In this context, science plays a vital role in proposing new solutions. The article aims to: (i) to carry out a bibliometric review of decision models in uncertainty through scientific mapping and performance analysis between 1990 and 2020; (ii) to know the scientific progress of 17 models that specialists validated. The Web of Science database and the VOSviewer, R, and Python software analyzed 26,835 articles in nine bibliometric indicators. The results revealed a positive trend of the publications in the analyzed models, being the Analytic Hierarchical Process the most used. Other findings showed China as the country with more scientific collaborations. There is enormous potential for future lines of research on the subject

Predictive long-term asset maintenance strategy: development of a fuzzy logic condition-based control system

Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceTechnology has accelerated the growth of the Facility Management industry and its roles are broadening to encompass more responsibilities and skill sets. FM budgets and teams are becoming larger and more impactful as new technological trends are incorporated into data-driven strategies. This new scenario has motivated institutions such as the European Central Bank to initiate projects aimed at optimising the use of data to improve the monitoring, control and preservation of the assets that enable the continuity of the Bank's activities. Such projects make it possible to reduce costs, plan, manage and allocate resources, reinforce the control, and efficiency of safety and operational systems. To support the long-term maintenance strategy being developed by the Technical Facility Management section of the ECB, this thesis proposes a model to calculate the Left wear margin of the equipment. This is accomplished through the development of an algorithm based on a fuzzy logic system that uses Python language and presents the system's structure, its reliability, feasibility, potential, and limitations. For Facility Management, this project constitutes a cornerstone of the ongoing digital transformation program

Decision support systems (DSS) for wastewater treatment plants: a review of the state of the art

The use of decision support systems (DSS) allows integrating all the issues related with sustainable developmentin view of providing a useful support to solve multi-scenario problems. In this work an extensive review on theDSSs applied to wastewater treatment plants (WWTPs) is presented. The main aim of the work is to provide anupdated compendium on DSSs in view of supporting researchers and engineers on the selection of the mostsuitable method to address their management/operation/design problems. Results showed that DSSs weremostly used as a comprehensive tool that is capable of integrating several data and a multi-criteria perspective inorder to provide more reliable results. Only one energy-focused DSS was found in literature, while DSSs based onquality and operational issues are very often applied to site-specific conditions. Finally, it would be important toencourage the development of more user-friendly DSSs to increase general interest and usability.This work is part of a research project supported by grant of the Italian Ministry of Education, University and Research (MIUR) through the Research project of national interest PRIN2012 (D.M. 28 December 2012 n. 957/Ric – Prot. 2012PTZAMC) entitled “Energy consumption and Greenhouse Gas (GHG) emissions in the wastewater treatment plants: a decision support system for planning and management – http://ghgfromwwtp.unipa.it” in which the first author is the Principal Investigator. In addition, some coauthors acknowledge the partial support of the Industrial Doctorate Programme (2017-DI-006) and the Research Consolidated Groups/Centres Grant (2017 SGR 574) from the Catalan Agency of University and Research Grants Management (AGAUR), from Catalan Government.Peer ReviewedPostprint (author's final draft

Fuzzy-GIS development of land evaluation system for agricultural production in North West Libya

The continuing deterioration of land and water resources occurring in several regions of the world is partly as a result of the mismatch between land suitability or capability and land use. Failure to achieve a perfect match between land capability and use can be particularly problematic for agricultural production because cultivating the wrong crops on wrong soils can only result in poor yields and its associated financial and other losses. There is therefore, a pressing need for effective land evaluation through better matching of land characteristics with land use to achieve optimal utilisation of available land resources for sustainable agricultural production. As far as agriculture is concerned such an exercise will result in defining which part of an area is suitable for particular crops, based on the available land resources and other production inputs, and which parts are better left for other uses. In this study, a land evaluation system for predicting the physical suitability of land for key crops, namely Wheat, Barley and Olive in the north west of Libya was developed based on matching land use requirement for these crops with the available land resources in the area. It involved a modelling strategy based on Boolean and Fuzzy logic sets, implemented within a Geographic Information System (GIS) environment. While the Boolean method assumes that the attributes of a given soil type are known with certainty and the boundaries between soil types are clearly defined, Fuzzy logic can be used to accommodate uncertainties in the available knowledge on these attributes through the use of membership functions. The GIS-based models developed comprise four layers; namely, soil, climate, slope and erosion hazard all of which have been shown directly influence land suitability for agricultural production. This resulted in the classification of the soil into 4 suitability classes, i.e. high suitability, moderate suitability, marginal suitability and not suitable. The results show that for Barley for example 52% of the soil in the north western Libya is highly suitable using Fuzzy approach while the corresponding figure for the Boolean is 62%. The two approaches were compared on cell by cell basis using map agreement. The comparison shows that there were reasonable agreements in evaluations by the two approaches for barley, wheat and olive of 51%, 46% and 56% respectively

Development and integration of environmental evaluation tools for the ecodesign of sustainable processes and products

Industry is recognized as one of the main sources of environmental pollution and resource depletion, both causing environmental degradation; nonetheless, its contribution to development and wealth creation is also acknowledged. Therefore, the identification of sustainable options in this area is a key factor. Nowadays, the attitude towards pollution prevention and control and cleaner production is not just a response to emerging environmental laws and regulations (Registration, Evaluation, Authorization and Restriction of Chemicals -REACH-, Integrated Pollution Prevention and Control –IPPC- Law, Integrated Product Policy –IPP-), but also a matter of corporate responsibility. Further, it has proved to be a way to increase profits. The sustainability definition has received certain criticism for its vagueness, ambiguity and difficulty to translate this concept at different levels. To overcome the difficulties of its implementation, a wide variety of indicators have been developed and applied over the years, providing metrics essential at the action level. This thesis poses a contribution to the development of environmental evaluation tools adapted to particular production sectors, aiming at providing metrics to guide decision making for the ecodesign of sustainable processes and products. Integrative frameworks that combine methodologies of different nature were proposed as the most suitable way to achieve comprehensive evaluations. At the same time, the simplicity of tools was pursued to make its application easier and more attractive for enterprises, avoiding the need of in depth training