An Integrated Multi-Criteria System to Assess Sustainable Energy Options: An Application of the Promethee Method

The planning and appraisal of sustainable energy projects involve rather complex tasks. This is due to the fact that the decision making process is the closing link in the process of analysing and handling different types of information: environmental, technical economic and social. Such information can play a strategic role in steering the decision maker towards one choice instead of another. Some of these variables (technical and economic) can be handled fairly easily by numerical models whilst others, particularly ones relating to environmental impacts, may only be adjudicated qualitatively (subjective or not). In many cases therefore, traditional evaluation methods such as cost-benefit analysis and the main economic and financial indicators (NPV, ROI, IRR etc.) are unable to deal with all the components involved in an environmentally valid energy project. Multi-criteria methods provide a flexible tool that is able to handle and bring together a wide range of variables appraised in different ways and thus offer valid assistance to the decision maker in mapping out the problem. This paper sets out the application of a multi-criteria method (PROMETHEE developed by J.P. Brans et al. 1986) to a real life case that is in tune with the objectives of sustainable development.Renewable energy, Multicriteria, Sustainable devolopment

Electric load analysis using an artificial neural network

Load forecasting in the current, increasingly liberalized, electricity power market is of crucial importance as a means for producers to optimize and rationalize energy supply. A number of electric power companies are equipped to make forecasts with the aid of traditional statistical methods. This paper presents the use of an artificial neural net to an hourly based load forecasting application for a small electric grid on an Italian island (Lipari) not connected to the mainland. The aim was to examine the forecasting ability of a neural net in a situation where the electric load was subject to considerable seasonal variations over the year. The variations are affected by energy demand related to the tourism season as well as by climatic conditions, especially temperature. The network developed was a multi-layer perceptron type built on three layers trained with a back-propagation algorithm. The input layer receives all the most relevant information regarding: the class of day type, the hour in the daytime, the load and background temperature recorded at the indicated time for the months of March, August and October whilst the output layer provides the forecast value at the indicated time in December. The results obtained are encouraging; in the training phase the RMS error rate was around 2% and this rate settled at about 2.6% during testing. As both the margins of error recorded are acceptable, the use of a neural network for electric load forecasting applications can be considered an attractive option. Copyright © 2005 John Wiley & Sons, Ltd

A Takagi-Sugeno Fuzzy Inference System for Developing a Sustainability Index of Biomass

One aspect of the use of biomass for energy purposes which remains controversial concerns their full environmental sustainability. Considering the crucial importance of this problem, numerous authors have carried out evaluations of the environmental impact of the various types of biomass by means of several approaches. Although some of these methods are excellent environmental evaluation tools, they are unfortunately unable to manage uncertain input data. Instead, fuzzy-set based methods have proven to be able to deal with uncertainty in environmental topics. The original contributions proposed by fuzzy logic relate, on the one hand, to the representation of uncertain and vague information, and, on the other, to handling such information using fuzzy rules. A fuzzy inference system (FIS) constitutes the practice of framing mapping from the input to an output using fuzzy logic. In this paper, we propose an application of Takagi-Sugeno fuzzy inference modelling to build a synthetic index to assess the sustainability of production of the biomass for energy purposes

Evaluation of combined heat and power (CHP) systems using fuzzy shannon entropy and fuzzy TOPSIS

Combined heat and power (CHP) or cogeneration can play a strategic role in addressing environmental issues and climate change. CHP systems require less fuel than separate heat and power systems in order to produce the same amount of energy saving primary energy, improving the security of the supply. Because less fuel is combusted, greenhouse gas emissions and other air pollutants are reduced. If we are to consider the CHP system as "sustainable", we must include in its assessment not only energetic performance but also environmental and economic aspects, presenting a multicriteria issue. The purpose of the paper is to apply a fuzzy multicriteria methodology to the assessment of five CHP commercial technologies. Specifically, the combination of the fuzzy Shannon's entropy and the fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach will be tested for this purpose. Shannon's entropy concept, using interval data such as the α-cut, is a particularly suitable technique for assigning weights to criteria — it does not require a decision-making (DM) to assign a weight to the criteria. To rank the proposed alternatives, a fuzzy TOPSIS method has been applied. It is based on the principle that the chosen alternative should be as close as possible to the positive ideal solution and be as far as possible from the negative ideal solution. The proposed approach provides a useful technical–scientific decision-making tool that can effectively support, in a consistent and transparent way, the assessment of various CHP technologies from a sustainable point of view

ASSESSMENT OF SUSTAINABLE WASTEWATER TREATMENT TECHNOLOGIES USING INTERVAL-VALUED INTUITIONISTIC FUZZY DISTANCE MEASURE-BASED MAIRCA METHOD

Effective wastewater treatment has significant effects on saving water and preventing unnecessary water scarcity. An appropriate wastewater treatment technology (WWTT) brings economic benefits through reuse in different sectors and benefits the society and environment. This study aims to develop a decision-making framework for evaluating the sustainable WWTTs under interval-valued intuitionistic fuzzy set (IVIFS) environment. The proposed MCDM framework is divided into two stages. First, a new Hellinger distance measure is developed to determine the degree of difference between IVIFSs and also discussed its desirable characteristics. Second, an interval-valued intuitionistic fuzzy extension of multi-attribute ideal-real comparative analysis (MAIRCA) model is developed using the proposed Hellinger distance measure-based weighting tool. Further, the proposed model is implemented on an empirical study of sustainable WWTTs evaluation problem. Sensitivity and comparative studies are made. The results indicate that odor impacts, sludge production, maintenance and operation are the most effective sustainable factors and Microbial fuel cell (MFC) technology is the best WWTT followed by natural treatment methods

Application of Sustainability Principles for Harsh Environment Exploration by Autonomous Robot

Currently, the European Union (EU) is focusing on a large-scale campaign dedicated to developing a competitive circular economy and expanding the single digital market. One of the main goals of this campaign is the implementation of the sustainability principles in the development and deployment cycle of the new generation technologies. This paper focuses on the fast-growing field of autonomous mobile robots and the harsh environment exploration problem. Currently, most state-of-the-art navigation methods are utilising the idea of evaluating candidate observation locations by combining different task-related criteria. However, these map building solutions are often designed for operating in near-perfect environments, neglecting such factors as the danger to the robot. In this paper, a new strategy that aims to address the safety and re-usability of the autonomous mobile agent by implementing the economic sustainability principles is proposed. A novel multi-criteria decision-making method of Weighted Aggregated Sum Product Assessment—Single-Valued Neutrosophic Sets, namely WASPAS-SVNS, and the weight selection method of Step-Wise Weights Assessment Ratio Analysis (SWARA) are applied to model a dynamic decision-making system. The experimental evaluation of the proposed strategy shows that increased survivability of the autonomous agent can be observed. Compared to the greedy baseline strategy, the proposed method forms the movement path which orients the autonomous agent away from dangerous obstacles.This article belongs to the Special Issue Soft Computing for Sustainabilit

Algorithm Selection for Edge Detection in Satellite Images by Neutrosophic WASPAS Method

Nowadays, integrated land management is generally governed by the principles of sustainability. Land use management usually is grounded in satellite image information. The detection and monitoring of areas of interest in satellite images is a difficult task. We propose a new methodology for the adaptive selection of edge detection algorithms using visual features of satellite images and the multi-criteria decision-making (MCDM) method. It is not trivial to select the most appropriate method for the chosen satellite images as there is no proper algorithm for all cases as it depends on many factors, like acquisition and content of the raster images, visual features of real-world images, and humans’ visual perception. The edge detection algorithms were ranked according to their suitability for the appropriate satellite images using the neutrosophic weighted aggregated sum product assessment (WASPAS) method. The results obtained using the created methodology were verified with results acquired in an alternative way—using the edge detection algorithms for specific images. This methodology facilitates the selection of a proper edge detector for the chosen image content.This article belongs to the Collection Advanced Methodologies for Sustainability Assessment: Theory and Practic

An Integrated Fuzzy Goal Programming—Theory of Constraints Model for Production Planning and Optimization

Manufacturing companies are under constant pressure to optimize the economic sustain-ability of their production systems. Production planning and optimization is a well-established strategy for considering resource constraints and improving economic productivity. This study proposes an integrated fuzzy goal planning and the theory of constraints for production planning and optimization. To this end, a hybrid Delphi–Buckley method was used to identify the relevant goals and a paired matrix questionnaire was used to determine the fuzzy weights of the goals. Furthermore, a fuzzy bottleneck detection algorithm was used to deal with the bottlenecks. A case study in the cable industry is presented to demonstrate the applicability and exhibit the efficiency of the proposed model. The results indicate that production planning in the cable industry could experience less deviation, almost 11% less, from the goals by applying the fuzzy goal programming under the theory of constraints, compared to the traditional method or crisp-goal programming

Intraspecific crosses resulting in the first occurrence of eight and nine B chromosomes in Prochilodus lineatus (Characiformes, Prochilodontidae)

B chromosomes are supernumerary elements present in about 15% of eukaryotic species and are most frequently heterochromatic, behave parasitically, show a transmission rate higher than standard (A) chromosomes, and can provoke harmful effects on carriers. In the current work, Prochilodus lineatus individuals carrying eight and nine B chromosomes were obtained by induced crossing performed involving breeders with different B chromosome numbers in their cells. The high B chromosome numbers found in the offspring were recorded for the first time in this species. The use of cytogenetic techniques applied in the present study revealed that regardless of the increase in number of B chromosomes in the genome of these individuals, those elements did not presented active genes, and showed their normal heterochromatic characteristic

Parathyroid Retrospective Analysis of Neoplasms Incidence (pTRANI Study): An Italian Multicenter Study on Parathyroid Carcinoma and Atypical Parathyroid Tumour

Background: Parathyroid cancer (PC) is a rare sporadic or hereditary malignancy whose histologic features were redefined with the 2022 WHO classification. A total of 24 Italian institutions designed this multicenter study to specify PC incidence, describe its clinical, functional, and imaging characteristics and improve its differentiation from the atypical parathyroid tumour (APT). Methods: All relevant information was collected about PC and APT patients treated between 2009 and 2021. Results: Among 8361 parathyroidectomies, 351 patients (mean age 59.0 ± 14.5; F = 210, 59.8%) were divided into the APT (n = 226, 2.8%) and PC group (n = 125, 1.5%). PC showed significantly higher rates (p < 0.05) of bone involvement, abdominal, and neurological symptoms than APT (48.8% vs. 35.0%, 17.6% vs. 7.1%, 13.6% vs. 5.3%, respectively). Ultrasound (US) diameter >3 cm (30.9% vs. 19.3%, p = 0.049) was significantly more common in the PC. A significantly higher frequency of local recurrences was observed in the PC (8.0% vs. 2.7%, p = 0.022). Mortality due to consequences of cancer or uncontrolled hyperparathyroidism was 3.3%. Conclusions: Symptomatic hyperparathyroidism, high PTH and albumin-corrected serum calcium values, and a US diameter >3 cm may be considered features differentiating PC from APT. 2022 WHO criteria did not impact the diagnosis