Multi-criteria Decision-making Model Research Articles

Delineation of Groundwater Potential Using the Bivariate Statistical Models and Hybridized Multi-Criteria Decision-Making Models

Identifying groundwater potential zones in a basin and developing a sustainable management plan is becoming more important, especially where surface water is scarce. The main aim of the study is to prepare the groundwater potential maps (GWPMs) considering the bivariate statistical models of frequency ratio (FR), weight of evidence (WoE), and the multi-criteria decision-making (MCDM) model of Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) hybridized with FR and WoE. Two distance measures, Euclidean and Manhattan, were used in TOPSIS to evaluate their effect on GWPMs. The research focused on the Burdur Lake catchment located in the southwest of Türkiye. In total, 74 wells with high yields were chosen randomly for the analysis, 52 (70%) for training, and 22 (30%) for testing processes. Sixteen groundwater conditioning factors were selected. The area under the receiver operating characteristic (AUROC) and true skill statistics (TSS) were utilized to examine the goodness-of-fit and prediction accuracy of approaches. The TOPSIS-WoE-Manhattan model and the FR and WoE models gave the best AUROC values of 0.915 and 0.944 for the training and testing processes, respectively. The best TSS values of 0.827 and 0.864 were obtained by the TOPSIS-FR-Euclidean and WoE models for the training and testing processes, respectively.

Addressing the Global Logistics Performance Index Rankings with Methodological Insights and an Innovative Decision Support Framework

This study examines the Logistics Performance Index (LPI) rankings developed by the World Bank from a methodological perspective and proposes an alternative decision support framework. LPI serves as an interactive tool that helps countries identify challenges, innovative solutions, and opportunities in their trade and logistics sectors. In this study, the efficiency of logistics operations in 118 countries was evaluated using an integrated multi-criteria decision-making (MCDM) model objectively weighted by the Entropy method. Countries were ranked using the MCRAT, SAW, TOPSIS, and FUCA methods. According to the findings, large datasets provide more robust insights for sensitivity analyses, and wider weighting coefficient combinations make the data more meaningful. In addition, it is suggested to use low-compensation methods instead of classical additive methods for LPI. Unlike other studies in literature, this research applied an innovative sensitivity analysis to test the robustness of the model and comprehensively examined the effects of weighting techniques based on over 2500 different MCDM results. The findings suggest that the FUCA method should be recommended to decision-makers for calculating LPI rankings due to its simplicity, practicality, low compensatory power, and low sensitivity. This study offers methodological improvements when evaluating logistics performance and provides significant contributions to decision-making processes. The findings are expected to provide a valuable resource for policymakers and businesses in understanding a country’s position in global competition, as well as serving as a reference for researchers evaluating the logistics performance of countries.

Determining the appropriate concentration of an anesthetic mixture in three different fish species with the PROMETHEE decision model

Anesthesia is applied to protect fish welfare for reducing employee workload in aquaculture. The efficacy of the anesthetic agent varies depending on the fish species. In this study, the effect of a commercial anesthetic (VetiVital AquaSED) manufactured mainly with herbal extracts (includes eugenol, linalool, linalyl acetat etc.) on three different fish species (rainbow trout, common carp, and Danube sturgeon) was investigated. In addition, the best available concentration of the anesthetic mixture for each species was determined using the PROMETHEE decision model. Danube sturgeon showed more resistance to anesthetics than the other two species. It was determined that the increase in concentration caused histological deterioration in fish. On the other hand, hematological parameters were affected by the anesthetic mixture, and this effect returned to normal levels after 8 h. According to the multi-criteria decision model, the best available concentrations determined by considering 10 different criteria are 400, 480, and 675 mg L−1 for rainbow trout, common carp and Danube sturgeon, respectively. Future studies should focus on developing the most appropriate anesthesia mixture in terms of physiological and anesthesia effectiveness for the target fish species.

Design of a smart medical service quality evaluation system based on a hybrid multi-criteria decision model

As medical informatization continues to progress, the sophistication of smart medicine (SM) systems has led to a marked enhancement in the caliber of medical services provided. Nevertheless, the existing body of literature is sparse when it comes to frameworks for evaluating service quality, specifically within the domain of smart medicine (SM). Drawing on a hybrid multi-criteria decision-making model that fuses the best–worst method (BWM) with the VIKOR approach, this research has crafted an innovative framework for assessing the service quality of SM. BWM was used to obtain the weights of all dimensions and indicators under each dimension. Then, the service quality of hospitals H1, H2 and H3 in Xiamen was evaluated using the VIKOR method. The results showed that smart appointments, diagnosis and treatment are three important dimensions to evaluate SM service quality in medical institutions. The stability and robustness of the model were proved through sensitivity analysis. Findings suggest that hospitals can strengthen the construction of their appointment information platforms, the quality management of internal doctors, and the information connection between self-service terminals and information platforms to improve hospital service quality in the construction of smart medicine.

Optimizing public investments: A Sustainable Economic, Environmental, and Social Investment Multi-criteria Decision Model (SEESIM)

One of the most important aspects for the economic and social growth of a country is high efficiency of public investments. This imperative has never been more critical, especially in the context of Europe and Italy, where the integration of environmental, economic, and social goals is essential to address the multifaceted challenges of the 21st century. This research introduces the Sustainable Economic, Environmental, and Social Investment Model (SEESIM), a novel multi-criteria decision-making framework designed to ensure the sustainability of projects as their validity over time, especially from a National Recovery and Resilience Plan perspective. Utilizing the Analytic Hierarchy Process (AHP), SEESIM integrates environmental, economic, and social criteria as useful tool to evaluate a project in the post-funding phase, ensuring a balanced approach to efficiency and sustainability. The model is adaptable to various regional contexts, providing a transparent and replicable methodology for assessing the impacts and costs of public investments. An experimental scenario demonstrates SEESIM’s utility in guiding investment decisions, highlighting its potential to significantly enhance the effectiveness of public resources in achieving long-term sustainability goals. SEESIM represents a pivotal advancement in sustainable development strategies, offering a comprehensive tool for integrating multi-dimensional sustainability criteria into public investment decisions.

Multi-criteria decision-making model for sustainability functions integrated Industry 4.0 technologies within small and medium enterprises in emerging countries

PurposeIn the Industry 4.0 (I4.0) era, small and medium enterprises (SMEs) face significant pressure to make their manufacturing operations more sustainable. However, SMEs often lack the knowledge and information needed to leverage I4.0 technologies for achieving sustainability. This paper aims to develop a multi-criteria decision-making (MCDM) model to prioritize sustainability functions supported by I4.0 technologies in SMEs.Design/methodology/approachThe fuzzy-DEMATEL method was developed to classify sustainability functions based on their prominence and influence, categorizing them into cause-and-effect groups. Experts in I4.0 technologies, sustainability and SMEs from Brazil were consulted during the data collection phase. Sensitivity analysis was also performed to validate the results obtained through the DEMATEL method.FindingsThe results indicate that the most prominent and influential sustainability functions include organizing production processes, employee skill development and simplifying production planning and control. Therefore, investing in I4.0 technologies to support these functions can enable SMEs to amplify the impact of their sustainability initiatives and improve operational efficiency. The authors also observed that improving quality monitoring is the primary function within the effect group.Practical implicationsThis paper serves as a roadmap for SME managers seeking to enhance the effectiveness of their sustainability practices using I4.0 technologies, thereby maximizing impact while minimizing the complexity of implementation.Originality/valueThis study ranks key sustainability functions as actions and practices that SMEs can implement using I4.0 technologies to achieve sustainability. It also identifies the most prominent and influential functions managers should prioritize when investing in I4.0 technologies.

Hybrid multi-criteria decision-making model for assessing perceived significance of 23 potentially modifiable cancer risk factors among senior nursing officers

BackgroundPotentially modifiable cancer risk factors have been increasingly recognized among the Chinese population. In this study, we aimed to investigate the perceived significance of these risk factors among senior nursing officers, who play a crucial role in providing healthcare services. We also sought to determine senior nursing officers’ performance in reducing these risk factors.MethodsA questionnaire survey regarding 23 potentially modifiable cancer risk factors was conducted in November 2023 with 58 senior nursing officers at Taizhou Hospital in Zhejiang Province, China. The consistent fuzzy preference relation method and importance–performance analysis were used to determine the attribute weights and performance levels.ResultsThe senior nursing officers considered diabetes, ultraviolet radiation exposure, PM2.5 exposure, excess body weight, physical inactivity, alcohol consumption and secondhand smoking significant. However, performance in reducing secondhand smoking, physical inactivity, excess body weight, PM2.5 exposure, and ultraviolet radiation exposure required improvement.ConclusionsThe proposed hybrid multiple-criteria decision-making model enhances our understanding of the perceived significance of 23 modifiable cancer risk factors and performance in reducing them, which could facilitate improvements in health education efforts.

A multi-criteria decision-making model for sustainable selection of coastal protection structures

Although nature-based and hybrid (i.e., a combination of hard and nature-based) solutions provide well-documented benefits to both the environment and people living nearby, they cannot be implemented in all coastal zones. Thus, the selection process of coastal protection structures (CPSs) requires an in-depth and comprehensible assessment with an effective and robust decision-support framework. Existing frameworks in the literature, however, do not focus on the selection of nature-based solutions and environmental and social factors were not considered thoroughly in these frameworks. In this respect, this research aims to develop a decision-support model that guides decision-makers in selecting the most appropriate and sustainable CPSs. The framework was designed in a way that includes a variety of environmental and social factors along with economic and technical aspects. Then, fuzzy AHP and TOPSIS analyses were conducted to determine the relative importance of the selection factors and to select the most appropriate CPS. Factors such as sediment dynamics, geotechnical issues, construction cost, transportation and navigation effects, and technological know-how were among the top-rated factors. TOPSIS analysis results revealed that mangrove forests and artificial reefs were the top two most sustainable CPSs. The proposed framework can minimize the social and environmental impacts of CPSs and expedite the green transition in the coastal environment.

A multicriteria decision-making model for risk management in an integrated management system

PurposeThis paper proposes a multicriteria model for risk management to identify and assess risks associated with an integrated management system (IMS). The main benefit of the proposed model is its systemic and logical visualization, which may facilitate the understanding of this proposal’s practical application.Design/methodology/approachThe research design consists of four stages: (1) conduct a literature review to establish risk management models in IMS; (2) collect data concerning risk management models in IMS from a large multinational automotive company; (3) propose a multicriteria model to define and assess risks as well as prioritize mitigation actions and (4) apply the proposed multicriteria model to the data collected in case-based research to evaluate the practical viability of the model to contribute to methods traditionally used.FindingsThe results showed that the proposed risk management model contributes to more reliable decision-making in an IMS. The application of the proposed model identified 85 risks in the total processes of the IMS, 31 of which were classified as high risk; thus, priority actions to be taken were defined. The risk classification and prioritization facilitated the implementation of measures to mitigate or eliminate risks, as pointed out by the company managers.Research limitations/implicationsOne of the limitations is the fact that specific knowledge is required to maintain and update the multicriteria decision-making tool used in this study. Another one implies the approach to managing risks under the different ISO standards and sector-specific requirements, since this may require updates and customization of the proposed risk management model.Practical implicationsThe implementation of IMS in contemporary business environments can be supported by a robust risk management approach. In addition, it provides the leadership with a holistic view of multiple aspects related to a company and fosters continuous improvement.Social implicationsThe social implications of this study are assessed indirectly. This study contributes to the improvement of company management models.Originality/valueTraditionally, the methods used for risk management in IMS are usually applied independently of techniques such as failure mode and effect analysis. The model developed in this work enables to manage risks continuously to achieve a systemic view of organizational issues and greater transparency of the processes.

Photovoltaic Modules’ Cleaning Method Selection for the MENA Region

Photovoltaic (PV) systems are important components of the global shift towards sustainable energy resources, utilizing solar energy to generate electricity. However, the efficiency and performance of PV systems heavily rely on cleanliness, as dust accumulation can significantly obstruct their effectiveness over time. This study undertook a comprehensive literature review and carried out multiple interviews with experts in the PV systems field to propose a map for selecting the optimal PV cleaning method for PV systems within MENA region. These factors, covering meteorological conditions, the local environment, PV system design, module characteristics, dust deposition attributes, exposure time to dust, and socio-economic and environmental considerations, were employed as criteria in a Multi-Criteria Decision-Making (MCDM) model, specifically, an Analytic Network Process (ANP). The results indicate that partially automated cleaning is the most suitable method for existing utility-scale PV projects in the MENA region. The findings provide robust guidelines for PV system stakeholders, aiding informed decision-making and enhancing the sustainability of PV cleaning processes.

Examining thedrivers of forest cover change and deforestation susceptibility in Northeast India using multicriteria decision-makingmodels.

The increasing rates of forest cover change and heightened vulnerability to deforestation present significant environmental challenges in Northeast India. This study investigates the dynamics of forest cover changeand susceptibility to deforestation in this region from 2001 to 2021, utilizing data from the Hansen Global Forest Change (HGFC) productonthe Google Earth Engine (GEE) platform. A suite of multicriteria decision-making (MCDM) models-including VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), Simple Additive Weighting (SAW), Evaluation Based on Distance from Average Solution (EDAS), and Weighted Aggregates Sum Product Assessment (WASPAS)-was employed to assess changes in forest cover and deforestation susceptibility across varied zones. Multicollinearity tests confirmed the relevance of the factors influencing deforestation. Statistical validations, such as the Wilcoxon Signed Ranks Test, underscored the models' robustness, revealing statistically significant outcomes. Additionally, Receiver Operating Characteristic (ROC) curve and Area Under the Curve (AUC) analysis demonstrated the superior fit of the VIKOR model (AUC = 0.938) compared to SAW (AUC = 0.901), EDAS (AUC = 0.895), and WASPAS (AUC = 0.864) in predicting current deforestation susceptibility. Validation affirmed the reliability of all MCDM methods, with VIKOR displaying high sensitivity (True Positive Rate, TPR = 0.878) and optimal AUC (0.938). Correlation analyses among the models identified significant inter-relationships, notably a positive correlation between EDAS and SAW, and a negative correlation between VIKOR and SAW. The regions of Assam, Nagaland, Mizoram, and Arunachal Pradesh were identified as experiencing significant forest cover loss, indicating a pronounced susceptibility to future deforestation. These findings underscore the need for immediate intervention to address this critical environmental issue.

A Real-Time System Status Evaluation Method for Passive UHF RFID Robots in Dynamic Scenarios

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system’s real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system’s status in dynamic scenarios.

Exploring a fuzzy relationships among criteria for the development of community-based tourism in Malaysia: a multi-stakeholder perspective

PurposeThe purpose of the study was to identify and analyze the fuzzy relationships among criteria for the development of community-based tourism (CBT) in Malaysia through a multi-stakeholder lens. By exploring these relationships, the study aimed to provide valuable insights for stakeholders and policymakers to enhance the sustainability and effectiveness of CBT initiatives in Malaysia. The focus was on understanding the interplay between environmental, economic and socio-cultural criteria to guide decision-making and planning processes for sustainable tourism development.Design/methodology/approachThe study utilized a quantitative approach, specifically employing a fuzzy multi-criteria decision-making (MCDM) model known as decision-making trial and evaluation laboratory (DEMATEL). This methodology allowed for the integration of diverse perspectives from multiple stakeholders involved in CBT in Malaysia. By using DEMATEL, the researchers were able to analyze the complex relationships among sustainable criteria, identify causal factors and assess the interdependencies among environmental, economic and socio-cultural dimensions. The approach aimed to provide a comprehensive understanding of the criteria influencing the sustainability of CBT development in Malaysia.FindingsThe findings of the study revealed that environmental and economic factors emerged as pivotal causal criteria influencing socio-cultural outcomes in the development of CBT in Malaysia. The research highlighted the importance of multi-stakeholder collaboration in building sustainability in CBT, emphasizing the need for regular meetings, shared decision-making processes and clear communication channels among all involved parties. These insights empower stakeholders to make informed decisions and prioritize criteria for sustainable CBT development, ultimately contributing to the well-being of communities, preservation of natural and cultural heritage, and long-term environmental sustainability.Research limitations/implicationsThe study's limitations include the focus on CBT in Malaysia, which may limit the generalizability of the findings to other contexts. Additionally, the research relied on a quantitative approach, potentially overlooking qualitative nuances in stakeholder perspectives. Future studies could address these limitations by exploring diverse geographical locations and incorporating qualitative methods to capture a more comprehensive understanding of sustainable criteria in CBT development. These implications suggest opportunities for further research to validate and extend the findings in different contexts and with a broader range of stakeholder perspectives.Practical implicationsThe practical implications of the study include providing guidance for tourism policymakers and key stakeholders in developing effective policy interventions for CBT in Malaysia. By understanding the complex relationships among sustainable criteria, stakeholders can make informed decisions to foster environmental, economic and socio-cultural growth in CBT initiatives. The findings offer insights for creating economic opportunities, promoting fair trade practices and enhancing linkages between CBT and local businesses. These practical implications aim to support sustainable decision-making, planning processes and implementation strategies to ensure the long-term success and positive impact of CBT in Malaysia.Social implicationsThe social implications of the study underscore the importance of multi-stakeholder collaboration in CBT development in Malaysia. By engaging various stakeholders, including host communities, government agencies, non-governmental organizations (NGOs) and tourists, the research promotes social cohesion, cultural exchange and community empowerment. Enhancing social interactions and mutual understanding among stakeholders can lead to inclusive decision-making processes, equitable distribution of benefits and the preservation of local heritage and traditions. These social implications emphasize the potential for CBT to contribute to social well-being, cultural preservation and sustainable development, fostering positive social impacts and strengthening community resilience.Originality/valueThe originality and value of the study lie in its exploration of the complex relationships among sustainable criteria for CBT development in Malaysia from a multi-stakeholder perspective. By utilizing the DEMATEL method and quantitative analysis, the research offers a novel approach to understanding the causal factors influencing environmental, economic and socio-cultural outcomes in CBT initiatives. The study's findings provide valuable insights for stakeholders and policymakers to make informed decisions, prioritize criteria and develop effective strategies for sustainable CBT development. This original contribution enhances the understanding of sustainable criteria relationships and empowers stakeholders to drive positive change in the tourism sector.

Application of multi-criteria decision making (MCDM) model for solar plant location selection

This study aims to systematically evaluate and identify the most suitable locations for establishing solar farms in the United Kingdom. It employs a comprehensive Multi-Criteria Decision-Making (MCDM) model that incorporates the Fuzzy Analytic Hierarchy Process (FAHP) method. Eight potential locations have been identified and selected for their climatic conditions and other pertinent parameters. These potential locations underwent additional assessment considering several factors including temperature, annual hours of sunshine, absolute humidity, proximity to major roads, distance from the power network, and potential demand. The data was normalized to eliminate scale effects and improve comparability. A MATLAB program has been developed with universality and user-friendliness in mind. Its purpose is to establish location selection factors graphs according to application-specific criteria and generate the matrix representation of these graphs for each potential candidate location. The permanent matrix, a composite metric synthesizing six criteria, identifies Torquay as the optimal location with a permanent value of 18.16. This quantitative approach, intersected with qualitative assessments, presents a clear hierarchy of site suitability. The model's simplicity, scalability, and proven accuracy make it a valuable tool for assessing renewable energy project viability in diverse regions.

A new herbal anesthetic agent for common carp (Cyprinus carpio) sedation and anesthesia: nutmeg (Myristica fragrans) essential oil.

In aquaculture, interest in natural essential oils is increasing alongside synthetic anesthetic agents. In this context, the anesthetic efficacy of nutmeg essential oil, which had not been previously tested, was investigated in common carp (Cyprinus carpio). The study, conducted using three different concentrations (800 μL/L "LC", 1,200 μL/L "MC", and 1,400 μL/L "HC"), found that induction times were <3 min for MC and HC, while LC had a longer induction time, exceeding acceptable levels. Within the first 4 h, white blood cells, red blood cells, hemoglobin, and hematocrit levels increased to >5 103/μL, >1 106/μL, >6 g/dL, and >12%, respectively. However, they returned to control levels after 8 h. Histological signs were more severe with higher concentrations, and necrosis was only observed in the HC group. Alkaline comet assay results showed DNA migration only in the HC group. According to the PROMETHEE multi-criteria decision-making model, the LC concentration is suitable for sedation, while the MC concentration should be used for deep anesthesia. The current study demonstrates that nutmeg essential oil can be used as an alternative to commercial synthetic anesthetic agents.

Literature Review on Multi-Criteria Analysis and Application in Education Environment

Due to the increasing complexity of educational data, the use of decision analysis techniques such as Multi-Criteria Decision Making (MCDM) models has become more popular in the education system in recent years. Multi criteria decision making methods provide data analysis methods faster and more efficient for revealing unhidden patterns and other meaningful information from vast educational data those conventional analytics are unable to discover in a reasonable amount of time. Particularly, MCDM techniques have been demonstrated to be effective methods for pattern recognition in educational systems. Motivated by this consideration, the purpose of this paper is to investigate the MCDM approaches applied to education systems through a review of new architectures, applications, and educational trends. The primary objective of this paper is to provide extensive insight into the application of MCDM models to education solutions in order to bridge the gap between MCDM techniques and human-based education interpretability. Then, the application of MCDM to various aspects of education are categorized. Finally, we present the current open challenges and future directions.

Development of the rough Defining Interrelationships Between Ranked criteria II method and its application in the MCDM model

Although numerous methods of multi-criteria decision-making have been developed so far, there are still those that have not been improved by different theories that deal well with uncertainties and inaccuracies that are a normal occurrence in everyday life. The goal of this research is the development of the Rough Defining Interrelationships Between Ranked Criteria II (Rough DIBR II) method and the presentation of its application in the multi-criteria decision-making (MCDM) problem, specifically in human resource management (HRM). The paper presents the subject-developed method and its practical application in the MCDM model with the Rough Additive Ratio Assessment (Rough ARAS) method. In order to check the consistency and validity of the proposed methodology, a sensitivity analysis and a comparative analysis were performed. The conclusions of this research indicate a stable and valid proposed methodology, as well as the possible application of the Rough DIBR II method for defining weighting coefficients of criteria in real-life decision-making problems.

Evaluation of Soils Around the Crude Oil Desalting Plant by MCDM Method to Reduce Pollution in the Koreyt Camp Area of Ahvaz, Iran

ABSTRACT In order to identify the factors causing environmental pollution caused by petroleum industries in Ahvaz County, Khuzestan province, regarding local concerns, the area of soil affected by the operational activity of crude oil desalting plantation (CODP) should be determined. In addition, in order to identify obvious environmental risks leading to soil pollution, it is necessary to identify hazardous activities ‎(HA). MCDM methods, or multi-criteria decision-making models, for ranking options, and choosing the best option for identifying environmental risks. Therefore, in this research, by examining models of multi-criteria decision-making, including the AHP Analytical Hierarchy Process and the TOPSIS Model (Technique for Order of Preference by Similarity to Ideal Solution), HAs for determining the possibilities of contamination have been discussed. HAs are considered to determine the possibility of contamination. According to the results, the transfer of saline oil from the exploitation unit to the CODP, with a specific ‎vector weight of 0.214 in the AHP method and a proximity coefficient of 0.445 in the ‎TOPSIS method, gain highest rank in terms of HAs in the study area. Regarding the risk assessment and assurance of the creation of salinity and sometimes petroleum pollutants in the region, and also the local obligations and regulations of the founders, industrial devices should preferably use biological methods to remediate and naturalize the environment. According to the results, and regarding the fact that halophyte can grow and develop well in the saline conditions; halophyte culture (haloculture technique) can be used as a method for soil bioremediation.

Selection decision of optimal hydrogen storage salt cavern based on combination weight and cumulative prospect theory

With the background of ' energy revolution ' and ' double carbon ', clean energy has gradually become a global research hotspot, and hydrogen is the key to affect the future energy pattern. With the continuous expansion of the field of energy storage, underground hydrogen storage is widely considered to be an effective way to solve large-capacity long-term storage, in which salt caverns are considered because of their unique advantages. At present, most of the research on salt cavern selection starts from the salt cavern itself and ignores the environment and social groups, and the discussion is too one-sided. This paper evaluates the criteria from both subjective and objective aspects, and constructs a multi-criteria decision-making model for selecting salt caverns based on prospect theory. It aims to select the most suitable hydrogen storage salt caverns from the overall impact of society, environment, salt cavern characteristics and supply and demand sides. Taking Jintan area of Jiangsu Province in China as an example, this paper selects an optimal salt cavern and an alternative salt cavern from five salt caverns, and carries out double verification of sensitivity analysis and comparative analysis. This study will provide some reference for the field of underground hydrogen energy storage, and provide reference for relevant areas suitable for hydrogen storage.

A simulation-based techno-economic-safety-social-environmental decision-making framework for prioritizing plastic waste treatment processes

Chemical recycling technologies hold promising potential in converting mixed plastic waste into energy and chemicals. This study proposed a simulation-based quantitative life cycle sustainability assessment framework for the potential plastic waste valorization processes. Sustainability metrics including environment, economy, safety, society, and energy were quantified and comprehensively investigated. Based on the Bayesian best-worst weighting and vector-based ranking methods, the normalized and weighted life cycle sustainability scores were used to prioritize the best process. Gasification-based technology was found superior than incineration-based technology, especially in the economic, techno-energetic, and social aspects. However, incinerated-based process with direct CO2 emissions performed better in the environmental aspects compared with the process integrating with carbon capture and utilization unit. Additional environmental burdens were brought to the system when incorporating the carbon capture and methanol synthesis process. Increased system complexity might bring unexpected environmental burdens, and sometimes even outweighed the benefits. Various weights were assigned to the multi-criteria decision-making model, and the gasification-based process consistently ranked first across all scenarios.