Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions

© 2020 Elsevier Ltd Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid waste under different vermicompost treatments. Seven chemical and biological indices were studied as input variables to predict total nitrogen (TN) and total phosphorus (TP) recovery. The developed ANN and MLR models were compared by statistical analysis including R-squared (R2), Adjusted-R2, Root Mean Square Error and Absolute Average Deviation. The results showed that vermicomposting increased TN and TP proportions in final products by 1.5 and 16 times. The ANN models provided better prediction for TN and TP with R2 of 0.9983 and 0.9991 respectively, compared with MLR models with R2 of 0.834 and 0.729. TN and C/N ratio were key factors for TP and TN prediction by ANN with percentages of 17.76 and 18.33

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

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

Preliminary study in discovering 2-propen-1-one, 1-(2,4-dihydroxyphenyl)-3-(4-methoxyphenyl)- from syzygium aqueum leaves as a tyrosinase inhibitor in food product: experimental and theoretical approach

In this study, response surface methodology (RSM) in combination with central composite rotatable design (CCRD) were performed to optimize the extraction parameters for total phenolic content (TPC) on Syzygium aqueum (S. aqueum) leaves. The effect of operational conditions on the extraction of S. aqueum leaves using carbon dioxide (CO2) on TPC was investigated. The conditions used in the supercritical extraction with CO2 included temperatures of (40-70 °C), pressures (2200-4500 psi) and extraction time (40-100 min). The highest TPC (3.5893 mg GAE/mg) was obtained at optimum conditions of 55 °C, 3350 psi and 70 min. The major compound in the optimized crude extract was2-propen-1-one,1-(2,4Dihydroxyphenyl)-3-(4-methoxyphenyl)- (82.65 %) which was identified by GC-MS. COSMO-RS was introduced to study the σ-profile between CO2 and 2-propen-1-one,1-(2,4-Dihydroxyphenyl)-3-(4methoxyphenyl)-. Principal component analysis (PCA) was performed to classify major compound which exhibit similar chemical properties with selected control. 2-propen-1-one,1-(2,4-Dihydroxyphenyl)-3-(4methoxyphenyl)- has similar chemical properties with kaempferol as tyrosinase inhibitor. Molecular electrostatic potential (MEP) and molecular docking were plotted to investigate a recognition manner of 2-propen-1-one,1-(2,4-Dihydroxyphenyl)-3-(4-methoxyphenyl)-upon tyrosinase receptor

ESTIMATION OF GREENHOUSE GAS AND ODOUR EMISSIONS FROM COLD REGION MUNICIPAL BIOLOGICAL NUTRIENT REMOVAL WASTEWATER TREATMENT PROCESSES

Rising human populations and ever-increasing demand for potable water result in increased municipal wastewater production. The collection, treatment, and management of municipal wastewaters include energy-intensive processes leading to the generation and emission of greenhouse, potentially toxic, and odorous gases. The main goal of this thesis was to advance knowledge of greenhouse gas (including carbon dioxide, CO2; methane, CH4; and nitrous oxide, N2O) and smelly compound (including ammonia, NH3; and hydrogen sulphide, H2S) emissions from typical municipal wastewater treatment plants (MWTPs) to accurately describe their emission rate estimates (EREs) using operating parameters. This research included laboratory and field assessments of greenhouse gas (GHG) and odour emissions in conjunction with monitored operating parameters. Laboratory-scale reactors simulating open-to-air treatment processes including primary and secondary clarifiers and anaerobic, anoxic, and aerobic reactors, were used to monitor gas EREs using wastewater samples taken from the analogous MWTP processes in winter and summer seasons. The Saskatoon Wastewater Treatment plan (SWTP) is a state-of-the-art biological nutrient removal (BNR) type MWTP and a Class IV treatment facility in Canada which was selected as a case study given its highly variable seasonal temperatures from −40 °C to 30 °C and its geographic location near the University of Saskatchewan. The experimental results were then used to develop a variety of novel machine learning models describing gas EREs with further optimization of operating parameters using genetic algorithm (GA). Studied machine learning models were artificial data generation algorithms (including generative adversarial network, GAN) and data-driven models (including artificial neural network, ANN; adaptive network-based fuzzy inference systems, ANFIS; and linear/non-linear regression models). To my knowledge, this is the first application of GAN used for MWTP modelling purposes. Results indicated that anaerobic digestion EREs averagely reached 4,443 kg CH4/d, 9,145 kg CO2/d, and 59.7 kg H2S/d. In contrast, GHG and odour ERE variabilities given ambient temperature changes were more noticeable for open-to-air treatment processes such that the winter EREs were 45,129 kg CO2/d, 21.9 kg CH4/d, 3.20 kg N2O/d, and insignificant for H2S and NH3. The higher temperature for the summer samples resulted in increased EREs for CH4, N2O, and H2S EREs of 33.0 kg CH4/d, 3.87 kg N2O/d, and 2.29 kg H2S/d, respectively, and still insignificant NH3 emissions. However, the CO2 EREs were reduced to 37,794 kg CO2/d, and interestingly, NH3 emissions were still negligible. Overall, the aerobic reactor was the dominant source of GHG emissions for both seasons, and changes in the aerobic reactor aeration rates (in reactor) and BNR treatment configurations (from site) further impacted the EREs. The integration of field monitoring data with data-driven models showed that the ANN, ANFIS, and regression models provided reasonable EREs using: (1) volatile fatty acids, total/fixed/volatile solids, pH, and inflow rate for anaerobic digestion biogas generations; and (2) hydraulic retention time, temperature, total organic carbon, dissolved oxygen, phosphate, and nitrogen concentrations for aerobic GHG emissions. However, when both model accuracy and uncertainty were considered there appears to be a compromise between these parameters with no model having simultaneously both high accuracy and low uncertainty. Additionally, and interestingly, virtual data augmentation using GAN was found to be a valuable resource in supplementation of limited data for improved modelling outcomes. GA was also coupled with the data-driven models to determine optimal operating parameters resulting in either GHG emission maximization given biogas could be beneficial for energy generation or GHG emission minimization given the aerobic reactor is an open-to-air process that can impact nearby residential neighbourhood air quality. The current study provides a hybrid methodology of mathematical modelling and experiments that can be used to accurately estimate and optimize the GHG and odour EREs from other MWTPs in Canada and worldwide

Predicting Escherichia coli loads in cascading dams with machine learning: An integration of hydrometeorology, animal density and grazing pattern

Accurate prediction of Escherichia coli contamination in surface waters is challenging due to considerable uncertainty in the physical, chemical and biological variables that control E. coli occurrence and sources in surface waters. This study proposes a novel approach by integrating hydro-climatic variables as well as animal density and grazing pattern in the feature selection modeling phase to increase E. coli prediction accuracy for two cascading dams at the USMeat Animal Research Center (USMARC), Nebraska. Predictive models were developed using regression techniques and an artificial neural network (ANN). Two adaptive neuro-fuzzy inference system (ANFIS) structures including subtractive clustering and fuzzy c-means (FCM)clusteringwere also used to developmodels for predicting E. coli. The performances of the predictive models were evaluated and compared using root mean squared log error (RMSLE). Cross-validation and model performance results indicated that although themajority of models predicted E. coli accurately, ANFIS models resulted in fewer errors compared to the othermodels. The ANFISmodels have the potential to be used to predict E. coli concentration for intervention plans and monitoring programs for cascading dams, and to implement effective best management practices for grazing and irrigation during the growing season

Gasification for Practical Applications

Although there were many books and papers that deal with gasification, there has been only a few practical book explaining the technology in actual application and the market situation in reality. Gasification is a key technology in converting coal, biomass, and wastes to useful high-value products. Until renewable energy can provide affordable energy hopefully by the year 2030, gasification can bridge the transition period by providing the clean liquid fuels, gas, and chemicals from the low grade feedstock. Gasification still needs many upgrades and technology breakthroughs. It remains in the niche market, not fully competitive in the major market of electricity generation, chemicals, and liquid fuels that are supplied from relatively cheap fossil fuels. The book provides the practical information for researchers and graduate students who want to review the current situation, to upgrade, and to bring in a new idea to the conventional gasification technologies

Machine learning for sustainable organic waste treatment: a critical review

Data-driven modeling is being increasingly applied in designing and optimizing organic waste management toward greater resource circularity. This study investigates a spectrum of data-driven modeling techniques for organic treatment, encompassing neural networks, support vector machines, decision trees, random forests, Gaussian process regression, and k-nearest neighbors. The application of these techniques is explored in terms of their capacity for optimizing complex processes. Additionally, the study delves into physics-informed neural networks, highlighting the significance of integrating domain knowledge for improved model consistency. Comparative analyses are carried out to provide insights into the strengths and weaknesses of each technique, aiding practitioners in selecting appropriate models for diverse applications. Transfer learning and specialized neural network variants are also discussed, offering avenues for enhancing predictive capabilities. This work contributes valuable insights to the field of data-driven modeling, emphasizing the importance of understanding the nuances of each technique for informed decision-making in various organic waste treatment scenarios

Multivariate Analysis in Management, Engineering and the Sciences

Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics. The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, association sand dependencies, in the areas of Management, Engineering and Sciences. The book is addressed to both practicing professionals and researchers in the field