Optimal evaluation of crop residues for gasification in Ghana using integrated multi-criterial decision making techniques

Lack of sustainable feedstock quantity and quality has been reported to be one of the major challenges confronting operations of gasifier plants in Ghana. In this paper, TOPSIS (Technique for order of Preference by Similarity to Ideal Solution) COPRAS (Complex Proportional Assessment) and VIKOR (Multi-criteria Optimisation and Compromise Solution) were used to select optimal crop residues for sustainable gasification in Ghana among thirteen residue types. The Analytical Hierarchy Process (AHP) was used as weighting criteria for the three Multi-Criteria Decision-Making (MCDM) techniques. Thirteen criteria based on particle size, proximate, ultimate analysis, calorific values, and quantity of residues were used. Five scenarios were considered; ranking of individual feedstock, consideration of the combination of the feedstock grouped under four categories. The ultimate and proximate analysis of the various crop residues as determined were generally within recommended levels as reported in the literature. Cashew shells and cassava peels have the highest heating value and annual technical residue potential of 23.4 MJ/kg and 880,750 tonnes respectively. Ranking of the individual crop residues confirmed cassava peels as the best alternatives of all the three MCDM techniques. The average rankings of the alternatives from the three MCDM in the order of best to worst are cassava peels, maize stalk, cocoa pod husk, maize cobs, rice straw, shea nut husk, groundnut husk, Palm kernel shells, EFB, rice husk, coconut husk/shells, cashew shells, and shea nut cake. Rankings of the alternatives under the four categories considered showed that feedstock blends containing four residues or more offer better opportunities for sustainable gasification than individual feedstock. Statistical analysis shows that there is a very strong, positive correlation between VIKOR, COPRAS, and TOPSIS. The strongest correlation is between VIKOR and TOPSIS with spearman's rank correlation index of 0.98. The analysis was sensitive to the weight of the strategy of group utility, recoverability ratio, residue-to-product ratio and annual crop production figures. It is recommended that optimal gasifier design and operational conditions taken into consideration the various feedstocks and their combination as determined from this study must be studied.

An extended picture fuzzy multicriteria group decision analysis with different weights: A case study of COVID-19 vaccine allocation

The high contagion rates of COVID-19 and the limited amounts of vaccines forced public health authorities to develop vaccinations strategies for minimizing mortality, avoiding the collapse of health care infrastructure, and reducing their negative impacts to societies and economies. We propose a Multi Criteria Group Decision Making for prioritizing a set of COVID-19 vaccination alternatives, under a picture fuzzy environment, where the weights for Decisions Experts (DE) and criteria are unknown. A panel of six DEs assess six criteria for prioritizing four groups for vaccination. The weights for DE and criteria are handled in the form of fuzzy sets. Three types of weights are calculated: subjective, objective, and mixture weights. According to our results, three out of the six criteria hold 60% of the strategic importance: 1) allocation and distribution, 2) COVID-19 strains and 3) capabilities and infrastructures. However, persons with comorbidities became the group with the highest priority, followed by essential workers, women, and adults older than 40 years. Governments, decision makers, and policy makers can find rigorous scientific evidence for articulating effective vaccinations campaigns from this work, and contribute to minimize undesired outputs, such as high mortality rates or collapse of hospitals.

Risk assessment approach for analyzing risk factors to overcome pandemic using interval-valued q-rung orthopair fuzzy decision making method

The process of developing and implementing sustainable strategies to prevent spread of COVID-19 for society typically requires integrating all social, technological, economic, governmental aspects in a systematic way. Since the clear understanding of risk factors contribute to the success of the strategies applied against COVID-19, a risk assessment procedure is applied in this study to properly evaluate risk factors cause to spread of pandemic as a multi-complex decision problem. Therefore, due to the evaluation of risk factors, which often involves uncertain information, the model is constructed based on interval-valued q-rung orthopair fuzzy-COmplex PRoportional ASsessment (IVq-ROF-COPRAS) method. While the developed framework is efficient to enhance the quality of decisions by implementing more realistic, precise, and effective application procedure under uncertain environment, it has capability to help governments for developing comprehensive strategies and responses. According to the results of the proposed risk analysis model, the top three risk factors are "The Approach that Prioritizes the Economy in Policies", "Insufficient Process Control in Normalization" and "Lack of Epidemic Management Culture in Individuals and Businesses". Lastly, to show applicability and efficiency of the model sensitivity and comparative analysis were conducted at the end of the study.

Fermatean fuzzy copula aggregation operators and similarity measures-based complex proportional assessment approach for renewable energy source selection

Selecting the optimal renewable energy source (RES) is a complex multi-criteria decision-making (MCDM) problem due to the association of diverse conflicting criteria with uncertain information. The utilization of Fermatean fuzzy numbers is successfully treated with the qualitative data and uncertain information that often occur in realistic MCDM problems. In this paper, an extended complex proportional assessment (COPRAS) approach is developed to treat the decision-making problems in a Fermatean fuzzy set (FFS) context. First, to aggregate the Fermatean fuzzy information, a new Fermatean fuzzy Archimedean copula-based Maclaurin symmetric mean operator is introduced with its desirable characteristics. This proposed operator not only considers the interrelationships between multiple numbers of criteria, but also associates more than one marginal distribution, thus avoiding information loss in the process of aggregation. Second, new similarity measures are developed to quantify the degree of similarity between Fermatean fuzzy perspectives more effectively and are further utilized to compute the weights of the criteria. Third, an integrated Fermatean fuzzy-COPRAS approach using the Archimedean copula-based Maclaurin symmetric mean operator and similarity measure has been developed to assess and rank the alternatives under the FFS perspective. Furthermore, a case study of RES selection is presented to validate the feasibility and practicality of the developed model. Comparative and sensitivity analyses are used to check the reliability and strength of the proposed method.

Evaluating and improving the sustainability of ecosystem services in river basins under climate change

This paper presents a new framework for evaluating the sustainability of basin-wide ecosystem services (ESs) including provisioning, regulating, supporting, and cultural services. In this framework, the Soil and Water Assessment Tool (SWAT) and MODSIM¹ models and experts' opinions are used to evaluate the ESs. To show the applicability of the proposed framework, it is applied to the Zarrinehrud river basin under three different climate change (CC) scenarios (i.e., RCP 4.5, 6.0, and 8.5) for two different time horizons (i.e., 2020-2049 and 2020-2098). This basin is the main water supplier of the largest hypersaline lake in the Middle East, Lake Urmia. In the next step, 128 water resources management (WRM) scenarios are taken into account considering the projects defined by Urmia Lake Restoration National Committee (ULRNC). All ecosystem services are evaluated considering all WRM and CC scenarios. Finally, a group COPRAS-based decision-making approach is used to determine the best WRM scenario under climate change. The results show that WRM scenario 128 is the best scenario for improving ecosystem services in the study area. This scenario includes some projects such as allocating water to the lake from new resources, rehabilitating irrigation and draining networks, and improving cropping patterns.

An integrated method for hybrid distribution with estimation of demand matching degree

Timely and effective distribution of relief materials is one of the most important aspects when fighting with a natural or a man-made disaster. Due to the sudden and urgent nature of most disasters, it is hard to make the exact prediction on the demand information. Meanwhile, timely delivery is also a problem. In this paper, taking the COVID-19 epidemic as an example, we propose an integrated method to fulfill both the demand estimation and the relief material distribution. We assume the relief supply is directed by government, so it is possible to arrange experts to evaluate the situation from aspects and coordinate supplies of different sources. The first part of the integrated method is a fuzzy decision-making process. The demand degrees on relief materials are estimated by extending COPRAS under interval 2-tuple linguistic environment. The second part includes the demand degrees as one of the inputs, conducts a hybrid distribution model to decide the allocation and routing. The key point of hybrid distribution is that each demand point could be visited by different vehicles and each vehicle could visit different demand points. Our method can also be extended to include both relief materials and medical staffs. A real-life case study of Wuhan, China is provided to illustrate the presented method.

Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment

COVID-19, which emerged in December 2019, has affected the entire world. Therefore, COVID-19 has been a subject of research in various disciplines, especially in the field of health. One of these studies was the report made by the Deep Knowledge Group (DKG) consortium in which safe regions for COVID-19 were determined. In the report, the main criteria of quarantine efficiency, government efficiency of risk management, monitoring and detection, health readiness, regional resilience, and emergency preparedness are used in the evaluation of countries and regions (alternatives). As the data and research structure used in this report are based on multi-criteria, the purpose of this study is to evaluate and analyse the safety levels of 100 regions in the world in terms of COVID-19 using Technique for Order Performance by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumsa Optimizacija I Kompromisno Resenje (VIKOR) and Complex Proportional Assessment (COPRAS) methods. The data and information required in the methods were obtained from a report prepared by the DKG. The results of the methods were compared with the ranking results presented in a report of the DKG. Accordingly, it has been observed that the method that provides the closest results to the results of the report is the COPRAS method, and the method that gives the most distant results is the VIKOR method.

Multiobjective Optimization of Fabrication Parameters of Jute Fiber/Polyester Composites with Egg Shell Powder and Nanoclay Filler

In the present study, a model is presented to optimize the fabrication parameters of natural fiber reinforced polyester matrix composites with dual fillers. In particular, jute fiber mat was chosen as reinforcement and eggshell powder (ESP) and montmorillonite nanoclay (NC) were selected as fillers. The weight per square meter (GSM) of the fiber, the weight percentage of ESP and NC have been chosen as independent variables and the influence of these variables on tensile, flexural and impact strength of the composite has been inspected. The permutations of the different combinations of factors are intended to accomplish higher interfacial strength with the lowest possible number of tested specimens. The experiments were designed by the Taguchi strategy and a novel multi-objective optimization technique named COPRAS (COmplex PRoportional ASsessment of alternatives) was used to determine the optimal parameter combinations. Affirmation tests were performed with the optimal parameter settings and the mechanical properties were evaluated and compared. Experimental results show that fiber GSM and eggshell powder content are significant variables that improve mechanical strength, while the nanoclay appears less important.

Novel ensembles of COPRAS multi-criteria decision-making with logistic regression, boosted regression tree, and random forest for spatial prediction of gully erosion susceptibility

Gully erosion is considered as a severe environmental problem in many areas of the world which causes huge damages to agricultural lands and infrastructures (i.e. roads, buildings, and bridges); however, gully erosion modeling and prediction with high accuracy are still difficult due to the complex interactions of various factors. The objective of this research was to develop and introduce three new ensemble models, which were based on Complex Proportional Assessment of Alternatives (COPRAS), Logistic Regression (LR), Boosted Regression Tree (BRT), Random Forest (RF), and Frequency Ratio (FR) for spatial prediction of gully erosion with a case study at the Najafabad watershed (Iran). For this purpose, a total of 290 head-cut of gullies and 17 conditioning factors were collected and used to establish a geospatial database. Subsequently, FR was used to determine the spatial relationship between the conditioning factors and the head-cut of gullies, whereas RF, BRT, and LR were used to quantify the relative importance of these factors. In the next step, three ensemble gully erosion models, named COPRAS-FR-RF, COPRAS-FR-BRT, and COPRAS-FR-LR were developed and verified. The Success Rate Curve (SRC), and the Prediction Rate Curve (PRC) and their areas under the curves (AUC) were used to check the performance of the three proposed models. The result showed that Soil group, geomorphology, and drainage density factors played the key role on the occurrence of the gully erosion. All the three models have very high degree-of-fit and the prediction performance, the COPRAS-FR-RF model (AUC-SRC = 0.974 and AUC-PRC = 0.929), the COPRAS-FR-BRT model (AUC-SRC = 0.973 and AUC-PRC = 0.928), and the COPRAS-FR-LR model (AUC-SRC = 0.972 and AUC-PRC = 0.926); therefore, it is concluded that they are efficient and new powerful tools which could be used for predicting gully erosion in prone-areas.

Environmental management partner selection for reverse supply chain collaboration: A sustainable approach

The Indian electronics industry is facing immense pressure to include sustainability practices in order to meet customer expectations, comply with legislation and create an edge over competitors. This growing need for inclusion of sustainability is driving original equipment manufacturers (OEMs) to collaborate with third-party reverse logistics providers (3PRLPs) to sustainably manage returns. Collaboration with 3PRLP will put OEMs in a stronger position regarding compliance with government legislation, improving upon their corporate image and customer satisfaction. It is a win-win situation for the OEM. On the other hand, from the 3PRLP's point of view, it is important to know the capabilities of the OEMs before entering into a collaborative endeavour. Hence, it is firstly necessary to identify the most appropriate attributes of the manufacturer which are essential for a sustainable collaboration. In this context, the study proposes a novel framework for investigating the performance attributes of the OEMs from the economic, environmental and social aspects of sustainability. Since the evaluation of each attribute for each dimension of sustainability differs in terms of the nature of the information needed and the time and resources required, it is necessary to conduct a pre-evaluation of the attributes using the Complex Proportional Assessment (COPRAS) method. Furthermore, the shortlisted attributes for each sustainability dimension are evaluated and prioritized using the best worst method (BWM). The novelty of the attribute selection process lies in simultaneously considering the degree of importance of the attribute as well as the degree of difficulty of the collection of data required for the assessment of the OEM. The result of this study helps decision-makers and practitioners to comprehend the most influential attributes of OEMs which are crucial for collaboration, thus enhancing the overall sustainability impact of the supply chain. The managerial implications drawn from the resulting analysis provide the 3PRLP with a sustainable evaluation framework which can be ideally used for the selection of collaborative partners. The above model is validated using the case of an Indian 3PRLP company that handles electronic products.