Electronic noses for environmental monitoring applications

Electronic nose applications in environmental monitoring are nowadays of great interest, because of the instruments’ proven capability of recognizing and discriminating between a variety of different gases and odors using just a small number of sensors. Such applications in the environmental field include analysis of parameters relating to environmental quality, process control, and verification of efficiency of odor control systems. This article reviews the findings of recent scientific studies in this field, with particular focus on the abovementioned applications. In general, these studies prove that electronic noses are mostly suitable for the different applications reported, especially if the instruments are specifically developed and fine-tuned. As a general rule, literature studies also discuss the critical aspects connected with the different possible uses, as well as research regarding the development of effective solutions. However, currently the main limit to the diffusion of electronic noses as environmental monitoring tools is their complexity and the lack of specific regulation for their standardization, as their use entails a large number of degrees of freedom, regarding for instance the training and the data processing procedures

Fundamentals in selecting input and output variables for composting process automatic controllers

El títol del pre-print és Review of fundamentals in selecting input and output variables for composting process automatic controllersThis paper provides a critical analysis of the fundamental principles involved in the selection of input and output variables for automatic controllers of composting processes. Research results and technological advancements make available a number of parameters which may be used by a composting process controller. Parameters based on ventilation have been identified as the most appropriate controller output variables. On-line monitoring of odour generation potential and pathogen destruction has not become feasible, although recent advances indicate potential for electronic noses and biosensors. On-line measurement of reaction rate heavily depends on the suitability of microbial respirometric methods. Water content of the material being composted may be useful in on-line evaluation of reaction rate if relationships between water loss rate and respiration rate are adequately described. Developments in artificial intelligence offer, however, new avenues regarding real-time estimation of reaction rates. In general, the first experiences from application of artificial intelligence on composting process control indicate potential for substantial utility

Municipal solid waste management system: decision support through systems analysis

Thesis submitted to the Universidade Nova de Lisboa, Faculdade de Ciências e Tecnologia for the degree of Doctor of Philosophy in Environmental EngineeringThe present study intends to show the development of systems analysis model applied to solid waste management system, applied into AMARSUL, a solid waste management system responsible for the management of municipal solid waste produced in Setúbal peninsula, Portugal. The model developed intended to promote sustainable decision making, covering the four columns: technical, environmental, economic and social aspects. To develop the model an intensive literature review have been conducted. To simplify the discussion, the spectrum of these systems engineering models and system assessment tools was divided into two broadly-based domains associated with fourteen categories although some of them may be intertwined with each other. The first domain comprises systems engineering models including cost-benefit analysis, forecasting analysis, simulation analysis, optimization analysis, and integrated modeling system whereas the second domain introduces system assessment tools including management information systems, scenario development, material flow analysis, life cycle assessment (LCA), risk assessment, environmental impact assessment, strategic environmental assessment, socio-economic assessment, and sustainable assessment. The literature performed have indicated that sustainable assessment models have been one of the most applied into solid waste management, being methods like LCA and optimization modeling (including multicriteria decision making(MCDM)) also important systems analysis methods. These were the methods (LCA and MCDM) applied to compose the system analysis model for solid waste. The life cycle assessment have been conducted based on ISO 14040 family of norms; for multicriteria decision making there is no procedure neither guidelines, being applied analytic hierarchy process (AHP) based Fuzzy Interval technique for order performance by similarity to ideal solution (TOPSIS). Multicriteria decision making have included several data from life cycle assessment to construct environmental, social and technical attributes, plus economic criteria obtained from collected data from stakeholders involved in the study. The results have shown that solutions including anaerobic digestion in mechanical biological treatment plant plus anaerobic digestion of biodegradable municipal waste from source separation, with energetic recovery of refuse derived fuel (RDF) and promoting pays-as-you-throw instrument to promote recycling targets compliance would be the best solutions to implement in AMARSUL system. The direct burning of high calorific fraction instead of RDF has not been advantageous considering all criteria, however, during LCA, the results were the reversal. Also it refers that aerobic mechanical biological treatment should be closed.Fundação para a Ciência e Tecnologia - SFRH/BD/27402/200

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Modern Applications of Electronic Nose: A Review

Electronic noses have provided a plethora of benefits to a variety of commercial industries, including the agricultural, biomedical, cosmetics, environmental, food, manufacturing, military, pharmaceutical, regulatory, and various scientific research fields. Advances have improved product attributes, uniformity, and consistency as a result of increases in quality control capabilities afforded by electronic-nose monitoring of all phases of industrial manufacturing processes. This paper is a review of some of the more important and modern applications that have been of greatest benefit to the humankind.DOI:http://dx.doi.org/10.11591/ijece.v3i1.122

Composting modelling : towards a better understanding of the fundamentals, applications for enhanced nutrient recycling, greenhouse gas reduction, and improved decision-making

Cette thèse de doctorat vise à consolider, développer et appliquer nos connaissances sur la modélisation du compostage, dans le but de fournir des informations, des outils et des perspectives accessibles et utilisables pour les chercheurs et les décideurs. L'espoir est que les travaux développés tout au long de cette thèse puissent aider à optimiser les procédés de compostage, notamment en réduisant les émissions de gaz à effet de serre (GES) et en améliorant le recyclage des nutriments. A ce titre, la thèse est divisée en trois phases : (1) la phase 1 est une consolidation et un développement des notions fondamentales de la modélisation du compostage, (2) suivie de la phase 2, où la modélisation de la perte de nutriments et des émissions de GES est étudiée, (3) avec la phase 3 qui est axée sur la manière d'assurer que ce travail puisse être utilisé par les décideurs et acteurs dans le milieu de compostage. Dans la première phase, une revue complète et systématique de l'ensemble de la littérature sur la modélisation du compostage a été entreprise (chapitre 2), cherchant à fournir une meilleure compréhension du travail qui a été fait et sur la direction des travaux futurs. Ceci a été suivi d'une étudie détaillée des approches de modélisation cinétique actuelles, notamment par rapport aux facteurs de corrections cinétiques appliqués à travers le domaine (chapitre 3). La phase 2 s'est ensuite focalisée sur les notions spécifiques relatives aux émissions de GES et aux pertes de nutriments lors du compostage, et à la modélisation de ces phénomènes. Cette thèse présente les réacteurs expérimentaux et le plan conçu pour suivre et évaluer le processus de compostage (chapitre 4), ainsi que le modèle de compostage compréhensif développé pour prédire avec précision les émissions et la transformation des nutriments pendant le compostage (chapitre 5). Enfin, la phase 3 visait à rendre ces informations facilement et largement utilisables. Cela a commencé par une évaluation des meilleures pratiques pour développer des modèles et des systèmes d'aide à la décision pour la prise de décision environnementale (chapitre 6), suivi par le développement de nouvelles approches de modélisation cinétique simples (chapitre 7), culminant avec le développement, l'ajustement paramétrique et la validation d'un modèle de compostage parcimonieux (chapitre 8). Grâce à ce travail, une base consolidée de l'état actuel de la modélisation du compostage a été développée, suivie par l'exploration et le développement de connaissances et d'outils à la fois fondamentaux et applicables.This PhD thesis aims consolidating, developing, and applying our knowledge on composting modelling, with the goal of providing accessible and usable information, tools, and perspectives for researchers and decision-makers alike. The hope is that the work developed throughout this dissertation can help in optimizing composting, notably by reducing greenhouse gas (GHG) emissions and improving nutrient recycling. As such, the thesis is divided into three phases: (1) phase 1 is a consolidation and development of the fundamentals of composting modelling, (2) followed by phase 2, where the modelling of nutrient loss and GHG emissions is investigated, (3) with phase 3 focusing on how to ensure that this work can be used by decision-makers. In the first phase, a comprehensive and systematic review of the entirety of the literature on composting modelling was undertaken (chapter 2), seeking to provide a better understanding on the work that has been done and guidance on where future work should focus and how it should be approached. This review then raised some interesting questions regarding modelling approaches, notably regarding modelling of composting kinetics, which was studied in detail through the evaluation of current modelling approaches (chapter 3). Phase 2 then focused on the specific notions relating to GHG emissions and nutrient loss during composting, and how to model these phenomena. This section starts with a presentation of the experimental reactors and plan designed to monitor and evaluate the composting process (chapter 4), followed by the comprehensive composting model developed to accurately predict emissions and nutrient transformation during composting (chapter 5). Finally, phase 3 aimed to make this information easily and widely usable, especially for decision-makers. This started with a review on the best practices to develop models and decision support systems for environmental decision-making (chapter 6), followed by the development of novel simple kinetic modelling approaches (chapter 7), culminating with the development, calibration, and validation of a parsimonious composting model (chapter 8). Through this work, a consolidated basis of the current state on composting modelling has been developed, followed-up by the exploration and development of both fundamental and applicable knowledge and tools

Optimising industrial food waste management

Global levels of food waste are attracting growing concern and require immediate action to mitigate their negative ecological and socio-economic ramifications. In the developed world, of the order of 20-40% of food waste is generated at the manufacturing stage of supply chains and is often managed in non-optimised ways leading to additional environmental impacts. This research describes a novel decision-support tool to enable food manufacturers to evaluate a range of waste management options and identify the most sustainable solution. A nine-stage qualitative evaluation tool is used in conjunction with a number of quantitative parameters to assess industrial food waste, which is then used to generate performance factors that enable the evaluation of economic, environmental and social implications of a range of food-waste management alternatives. The applicability of this process in a software-based decision-support tool is discussed in the context of two industrial case studies

A Competency Mapping for Educational Institution: Expert System Approach

This paper presents the development of expert system to assist in the operation of competence management in educational institution. The knowledge based consists of a rule-based expert system for the competence management and subsequent performance assessment. It is generally recognized that an expert system can cope with many of the common problems relative with the operation and control of the competence management process. In this work an expert system is developed which emphasize on various steps involved in the competence management process. The knowledge acquisition to develop this expert system involved an exhaustive literature review on competence management operation and interviews with experienced deans and the competence managers. The development tool for this system is an expert system shell

Determining optimal municipal solid waste management scenario based on best-worst method

Municipal solid waste (MSW) management is one of the most important issues in urban environments, especially in developing countries. In this work, a comprehensive assessment framework for MSW management is proposed to evaluate and screen the optimal scenario. The best-worst method (BWM) is utilized to determine the optimal weight of each criterion for each disposal scenario. However, the original BWM model is difficult to be solved globally. A linear model is presented to solve the model and an interval model is employed to verify the optimality of the linear model. The results indicate that the results of the linear model and interval model are consistent. A case study of MSW disposal in Qingdao City is used to demonstrate the application of the proposed method. The results indicate that a combination of landfilling, incineration technology with energy recovery facility is preferred for the current MSW management in Qingdao from the chosen criteria. The framework proposed in this work can be assisted to help the decision-makers to identify the priority sequence of MSW management scenarios