Sustainable desalination process selection: Decision support framework under hybrid information

Preliminary thermoeconomic analysis of combined parabolic trough solar power and desalination plant in port Safaga (Egypt)

FMEA-CM based quantitative risk assessment for process industries—A case study of coal-to-methanol plant in China

Vietnam’s garment industry is facing many challenges, including domestic competition and the global market. The free trade agreement, which Vietnam signed, includes environmental barriers, sustainable development, and green development. The agreement further requires businesses to make efforts to improve not only product quality but also the production process. In cases when enterprises cause environmental pollution in the production process and do not apply solutions to reduce waste, save energy, and natural resources, there is a risk of no longer receiving orders or orders being rejected, especially orders from the world’s major branded garment companies. In this research, the authors propose a multicriteria decision-making model (MCDM) for optimizing the supplier evaluation and selection process for the garment industry using sustainability considerations. In the first stage of this research, all criteria affecting supplier selection are determined by a triple bottom line (TBL) model (economic, environmental, and social aspects) and literature reviews; in addition, the fuzzy analytic hierarchy process (FAHP) method was utilized to identify the weight of all criteria in the second stage. The technique for order preference by similarity to an ideal solution (TOPSIS) is a multicriteria decision analysis method, which is used for ranking potential suppliers in the final stage. As a result, decision-making unit 10 (DMU/10) is found to be the best supplier for the garment industry. The contribution of this research includes modeling the supplier selection decision problem based on the TBL concept. The proposed model also addresses different complex problems in supplier selection, is a flexible design model for considering the evaluation criteria, and is applicable to supplier selection in other industries.

The Original TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) Method is a ranking method based on the principle that the chosen alternative should be as close to the ideal solution, rather than the negative-ideal solution, as possible. However, currently utilizing in establishing the evaluation system of indicator-based model, the Original TOPSIS Method is failed to take the weights of indices into account. Besides, other weighting method like Subjective Evaluation Method is proved to be largely influenced by various subjective situations. To resolve the problem of weighting, in this paper, we propose a new method named Modified TOPSIS Method utilizing Gray Correlation Analysis. By eliminating the mistakes caused by indices which are essentially measured by the same factors, it is useful to improve the evaluation system of indicator-based model. The verification utilizing Spearman's Rank Correlation Coefficient demonstrates that our Modified TOPSIS Method is improved.

BackgroundBy analyzing 23 evaluation indicators included in 14 national basic public health service programs in a region of Hohhot City, Inner Mongolia Autonomous Region, China, the performance of basic public health services in the region in 2021 were analyzed to clarify the implementation and conduct of relevant programs. We also use this study as a basis to radiate the work of municipal basic public health services centered on the region and the outstanding problems reflected and to provide theoretical contents and suggestions that can be referred to for the same regions in central and western China as well as worldwide.MethodsUsing the TOPSIS method as the basis for the data analysis method, the evaluation indexes are ranked in terms of their proximity to the idealized target, and combined with the entropy value method, Technique for order preference by similarity to an ideal solution (TOPSIS) and rank-sum ratio (RSR) were used to rank 14 basic health care providers by grade. A comprehensive evaluation of the performance of basic public health services in a region of Hohhot City, Inner Mongolia Autonomous Region in 2021 was conducted through a joint model of entropy -weighted TOPSIS and RSR, making full use of the characteristics and advantages of the fuzzy joint, and conducting a comprehensive analysis from the perspective of the ratio weight and the method of graded calculation, making the study more distinguishable and measureable.ResultsIn this study, for the regional basic public health services, a total of 23 evaluation indicators of basic public health service projects were included, among which the top three indicators with the weight of the entropy value method indicators were found to include the rate of Chinese medicine health management for the elderly, the rate of health management for the elderly, and the BCG vaccination rate after the analysis of the weight of the indicators; After the entropy-weighted TOPSIS evaluation showed that the Ci values of the regions were found to be between 0.378 and 0.715 through the calculation of the positive and negative ideal values of each indicator; RSR staging method evaluation showed that three community health centers (X2, X10, X12) had excellent evaluations of basic public health services; The number of evaluations as poor and moderate are 2 (X3, X9) and 9 (X1, X4, X5, X6, X7, X8, X11, X13, X14), respectively; Finally, the results of the entropy-weighted TOPSIS method and the fuzzy joint model of RSR staging method are basically consistent with the overall trend of the above two methods, and the reliability and credibility of the research results are high.ConclusionThe entropy-weighted TOPSIS and RSR joint model can evaluate the effectiveness of basic public health services in a more comprehensive and holistic way. The results of the RSR staging results and the related weight ratio analysis show that the basic public health service programs in Hohhot, Inner Mongolia Autonomous Region are relatively balanced, but there are some differences; The same genus of elderly Chinese medicine health management rate, health management rate of the elderly, BCG vaccination rate several indicators accounted for a higher weight, its correlation with the key population-related items is high, suggesting that the future key population health service items should be focused on, and future research should be suggested from two key research.

The Pebble Bed Modular Reactor (PBMR), under development in South Africa, is an advanced helium-cooled graphite moderated high-temperature gas-cooled nuclear reactor. The heat output of the PBMR is primarily suited for process applications or power generation. In addition, various desalination technologies can be coupled to the PBMR to further improve the overall efficiency and economics, where suitable site opportunities exist. Several desalination application concepts were evaluated for both a cogeneration configuration as well as a waste heat utilization configuration. These options were evaluated to compare the relative economics of the different concepts and to determine the feasibility of each configuration. The cogeneration desalination configuration included multiple PBMR units producing steam for a power cycle, using a back-pressure steam turbine generator exhausting into different thermal desalination technologies. These technologies include Multi-Effect Distillation (MED), Multi-Effect Distillation with Thermal Vapor Compression (MED-TVC) as well as Multi-Stage Flash (MSF) with all making use of extraction steam from backpressure turbines. These configurations are optimized to maximize gross revenue from combined power and desalinated water sales using representative economic assumptions with a sensitivity analysis to observe the impact of varying power and water costs. Increasing turbine back pressure results in a loss of power output but a gain in water production. The desalination systems are compared as incremental investments. A standard MED process with minimal effects appears most attractive, although results are very sensitive with regards to projected power and water values. The waste heat utilization desalination configuration is based on the current 165 MWe PBMR Demonstration Power Plant (DPP) to be built for the South African utility Eskom. This demonstration plant is proposed at the Koeberg Nuclear site and utilizes a direct, single shaft recuperative Brayton Cycle with helium as working fluid. The Brayton Cycle uses a pre-cooler and inter-cooler to cool the helium before entering the low-pressure compressor (LPC) and the high-pressure compressor (HPC) respectively. The pre-cooler and intercooler rejects 218 MWt of waste heat at 73°C and 63°C, respectively. This waste heat is ideally suited for some low temperature desalination processes and can be used without negative impact on the power output and efficiency of the nuclear power plant. These low temperature processes include Low Temperature Multi-Effect Distillation (LT-MED) as well Reverse Osmosis (RO) with pre-heated water. The relative economics of these design concepts are compared as add-ons to the PBMR-DPP and the results include a net present value (NPV) study for both technologies. From this study it can be concluded that both RO as well LT-MED provide water at reasonable production rates, although a final study recommendation would be based on site and area specific requirements.

A comprehensive and objective risk evaluation model of oil and gas pipelines based on an improved analytic hierarchy process (AHP) and technique for order preference by similarity to an ideal solution (TOPSIS) is established to identify potential hazards in time. First, a barrier model and fault tree analysis are used to establish an index system for oil and gas pipeline risk evaluation on the basis of five important factors: corrosion, external interference, material/construction, natural disasters, and function and operation. Next, the index weight for oil and gas pipeline risk evaluation is computed by applying the improved AHP based on the five-scale method. Then, the TOPSIS of a multi-attribute decision-making theory is studied. The method for determining positive/negative ideal solutions and the normalized equation for benefit/cost indexes is improved to render TOPSIS applicable for the comprehensive risk evaluation of pipelines. The closeness coefficient of oil and gas pipelines is calculated by applying the improved TOPSIS. Finally, the weight and the closeness coefficient are combined to determine the risk level of pipelines. Empirical research using a long-distance pipeline as an example is conducted, and adjustment factors are used to verify the model. Results show that the risk evaluation model of oil and gas pipelines based on the improved AHP–TOPSIS is valuable and feasible. The model comprehensively considers the risk factors of oil and gas pipelines and provides comprehensive, rational, and scientific evaluation results. It represents a new decision-making method for systems engineering in pipeline enterprises and provides a comprehensive understanding of the safety status of oil and gas pipelines. The new system engineering decision-making method is important for preventing oil and gas pipeline accidents.

The goal of tone recognition is to accurately identify the type and the name of the musical instruments through processing and analyzing the sound signals. In order to reduce the influence of feature confusion on classification process, a method of tone recognition based on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Random Forests is proposed. In this process, Mel Frequency Cepstral Coefficients (MFCCs) are acquired, and the quadratic sum of distance between two MFCCs and the entropy of information are computed which are used as indices to analyze and select the MFCCs based the TOPSIS decision. The selected MFCCs are used to classify tone of trumpet- piano, trumpet-cello and piano-cello, and the recognition rates were 100%, 99.9% and 100% respectively. The results are satisfactory and verify feasibility of the developed method.

Recently, energy saving problem attracts increasing attention from researchers. This study aims to determine the optimal arrangement of a tube bundle to achieve the best overall performance. The multi-objective genetic algorithm (MOGA) is employed to determine the best configuration, where two objective functions, the average heat flux q and the pressure drop Δp, are selected to evaluate the performance and the consumption, respectively. Subsequently, a decision maker method, technique for order preference by similarity to an ideal solution (TOPSIS), is applied to determine the best compromise solution from noninferior solutions (Pareto solutions). In the optimization procedure, all the two-dimensional (2D) symmetric models are solved by the computational fluid dynamics (CFD) method. Results show that performances alter significantly as geometries of the tube bundle changes along the Pareto front. For the case 1 (using staggered arrangement as initial), the optimal q varies from 2708.27 W/m2 to 3641.25 W/m2 and the optimal Δp varies from 380.32 Pa to 1117.74 Pa, respectively. For the case 2 (using in-line arrangement as initial), the optimal q varies from 2047.56 W/m2 to 3217.22 W/m2 and the optimal Δp varies from 181.13 Pa to 674.21 Pa, respectively. Meanwhile, the comparison between the optimal solution with maximum q and the one selected by TOPSIS indicates that TOPSIS could reduce the pressure drop of the tube bundle without sacrificing too much heat transfer performance.

Technique for order preference by similarity to an ideal solution (TOPSIS) is an effective technique to solve multi-criteria decision-making problem. It aims to select the alternative, which has the “shortest distance” from positive ideal solution (PIS) and the “farthest distance” from negative ideal solution (NIS). Nevertheless, much literature has demonstrated that the solution calculated by TOPSIS only is the compromise of PIS and NIS, and it is of great restriction in dealing with the practical problems which have diverse demands and properties. Therefore, in the presented model, an optimism coefficient is defined to expand the physical meaning of the standard TOPSIS. Decision-makers (DMs) can describe their attitudes toward risk and profit by changing the value of optimism coefficient. Furthermore, intuitionistic fuzzy number is introduced to measure the evaluations (linguistic values) of DMs to alternatives under diverse criteria. Intuitionistic fuzzy weighted averaging operator is used for fusing the judgments of all DMs. Finally, a conclusion can be safely obtained that the proposed model is stable and validity from the illustrative examples and sensitivity analysis.

During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The GRA(grey relational analysis)-based TOPSIS(technique for order preference by similarity to an ideal solution) decision-making approach, in combination with the advantages of both GRA and TOPSIS, was developed to solve multiple criteria decision making(MCDM) in fuzzy environment where attributes weights and ratings were expressed in interval numbers. A numerical example is proposed to illustrate an application of the proposed model. Finally, the direction for further research was pointed out.

Against the satellite heterogeneous network optimal selection problem, in this paper we propose a network selection algorithm based on the Analytic Hierarchy Process (AHP) method and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method. The algorithm considers the parameters that may affect the network selection like quality of business services, user preferences, network load, and so on. The use of AHP is to calculate the weights of various parameters, and TOPSIS is used to obtain the final access network ranking. The simulation results verify the correctness and validity of the algorithm.

Evaluation of technologies for a desalination operation and disposal in the Tularosa Basin, New Mexico

Detecting important actors in social networks is still a hot research topic in the complex network area. Various metrics of centrality are proposed to deal with this problem. Each metric addresses the problem from one point of view. Therefore, this paper introduces a new method to estimate the influence of nodes using multi-criteria decision-making MCDM. The proposed approach is based on the technique for order preference by similarity to an ideal solution (TOPSIS). TOPSIS aggregates centrality measures. Degree, betweenness, closeness, and eigenvector centralities are combined in this work to detect the importance of each actor. Each metric of centrality has its limitations, thus the proposed approach takes advantage of these four centralities together through TOPSIS. To illustrate the effectiveness of the proposed methodology, the Facebook ego network as an example of a social network is used for experimentation. The SI model is used to compare the spreading ability of nodes between centralities and the proposed approach on the Facebook ego network. The experimental results show that the proposed methodology can rank the important nodes more efficiently than a single measure of centrality.KeywordsComplex networksTOPSISImportant actorsCentrality measuresSI model

Ecological agriculture projects have achieved a growing development in the context of low carbon. However, because of the great difference in these issues from traditional types, there exist risks in progression quality and sustainability. To better identify the risk, this paper proposes a novel hybrid approach that integrates the analytic hierarchy process (AHP) with technique for order preference by similarity to an ideal solution (TOPSIS), as well as an improved support vector machine (SVM) based on the brainstorming algorithm (BSO). First, a risk evaluation index framework is developed and elaborated in terms of the natural environment, society, market economy, management, technology, and finance. Then the traditional assessment can be derived from AHP with TOPSIS. In addition, BSO is applied to improve SVM for rapid computation. Finally, a case study is implemented to verify the accuracy of the proposed technique. In this research, based on the low-carbon perspective, artificial intelligence algorithm and risk assessment are introduced into the field of ecological agriculture project management, which is conducive to the rapid and effective evaluation of ecological agriculture project risk. It can improve managers’ risk awareness and risk management ability, reduce investment blindness, and help ecological agriculture projects achieve healthy and sustainable development under the background of low carbon, thus contributing to the development of a low-carbon economy.