Development of A Hybrid Fuzzy-Stochastic Modeling Approach for Examining the Environmental Performance of Surface Flow Constructed

Storm water is considered as a significant source of contaminants to receiving rivers and the constructed wetland has been used to treat storm water before the discharge. In this study, a hybrid fuzzy-stochastic modeling approach is developed to examine the wetland treatment efficiency, to analyze the environmental impact associated with the wetland effluents into the receiving water, and to quantify system uncertainties. The proposed approach first incorporates a water quality model to simulate storm water flow going through the wetland and the fate and transport of nutrients in the wetland. A Monte Carlo modeling method is next developed to extend the water quality model, providing a stochastic simulation of the concentration distribution of nutrients in the wetland effluents. It is intended for the analysis of probabilistic environmental risks associated with wetland effluents on the receiving waters. The fuzzy membership functions are further used to quantify the variability or suitability of regional surface water guidelines, which is incorporated into the Monte Carlo modeling framework to identify the integrated risks from the discharge on the river. The developed modeling approach has been applied to the Kennedale wetland, a storm water treatment system, in the city of Edmonton, Canada. Before the environmental risk assessment, the HEC-RAS (Hydrologic Engineering Centers River Analysis System) model and the QUAL2K (River and Stream Water Quality) model are applied to simulate the flow and nutrients removal efficiency in the wetland. According to the simulation results from the HEC-RAS model and the QUAL2K model, the removal efficiencies of TN (Total Nitrogen) by the wetland are 25.64% and 13.59%, respectively. The removal efficiencies of TP (Total Phosphorus) are 50% and 50.91%, respectively. The differences between the HEC-RAS simulation results and on-site field data are 0.05% for TN and 6.1% for TP. The differences between the QUAL2K simulation results and on-site field data are 13.99% for TN and 4.35% for TP based on this study. The water quality simulation results from the two models are both acceptable compared to the monitoring data. It is seen that the HEC-RAS model has better performance on modeling this field case, and is integrated with the environmental risk assessment process. Consequently, the results of the integrated risk assessment referring to different guidelines in the North America show that the concentrations of TN at the wetland discharge port have a high possibility of violating the TN guidelines in both Alberta, Canada and the US EPA (Environmental Protection Agency). Similarly, the concentrations of TP at the wetland discharge port have a high possibility to violate the Canadian and US TP guideline during this study period. Therefore, the nutrients in storm water discharges from the Kennedale wetland may have a great risk to adversely affect the receiving river (North Saskatchewan River) at the time of this study. The analysis results of nutrient guidelines have supported the management of decision making process, and the study results indicate that the developed hybrid fuzzy-stochastic modeling approach is a useful tool for the practical managing of wetland systems and the impact of the wetland discharges on the receiving waters

Development of an Integrated System for the Simulation and Assessment of Produced Water Discharges from Offshore Platforms

Techniques for modeling of marine pollution have been studied for decades. Specialized modeling methods have been used to simulate the dispersions of pollutants from offshore outfalls. Produced water, the largest volume waste stream discharged from offshore oil and gas production activities, is a complex mixture of dissolved and particulate organic and inorganic chemicals including metals and hydrocarbons. In recent years, the growing importance and interest in the ocean environment assessment has urged further evaluation of produced water impacts on the marine ecosystem. This thesis study describes an integrated system for the modeling and assessment of produced water discharges in coastal area. The system integrates ocean circulation simulation, pollutant fate and transport modeling that couples near field mechanisms and far field processes, and risk assessment approaches where exposure risks and probabilistic risks are evaluated. A literature survey is first introduced to review and present capabilities and limitations of the most widely used methods and models associated with assessment of the impact of marine pollution. This review identified the need for an integrated system with configurations of numerical schemes of Princeton Ocean Model (POM) for ocean circulation simulation, a Lagrangian method to simulate near field transport processes in three dimensional cross flows, and a numerical solution for far field transport modeling. The physical models are dynamically integrated to ensure mass and energy conservation. Furthermore to assess risks, a modified Monte Carlo method which uses a statistical model to establish the relationship between uncertainty parameters and output concentrations is integrated with physical modeling system along with risk characterization approaches to map risk levels. Evaluation and field validations are conducted for each individual sub-models and for the overall integrated modeling results. Specifically, the near field model is validated against a field study performed in USA platform located about 100 miles of New Orleans Louisiana. The computational efficiency and accuracy of the far field model are evaluated through test cases in comparison with concentration distribution results generated from an exact analytical solution and a RWPT (Random Walk Particle Tracking) method. Validations of ocean circulation results and the integrated produced water dispersion results are conducted in a case study carried out on the Grand Banks of Newfoundland, Canada. Validations show good performance of the developed modeling system which is used to provide satisfactory 3D simulation of marine pollutant dispersion for effective assessment and management of offshore waste discharges. Finally, a risk assessment is carried out to predict risks associated with predicted lead and benzene concentration resulting from potential future produced water discharges in the East Coast of Canada. This research study provide a tool for the modeling of complex transport processes in the coastal area, and improved methods for risk assessment of produced water impacts on the regional water environment

Sustainability Analysis and Environmental Decision-Making Using Simulation, Optimization, and Computational Analytics

Effective environmental decision-making is often challenging and complex, where final solutions frequently possess inherently subjective political and socio-economic components. Consequently, complex sustainability applications in the “real world” frequently employ computational decision-making approaches to construct solutions to problems containing numerous quantitative dimensions and considerable sources of uncertainty. This volume includes a number of such applied computational analytics papers that either create new decision-making methods or provide innovative implementations of existing methods for addressing a wide spectrum of sustainability applications, broadly defined. The disparate contributions all emphasize novel approaches of computational analytics as applied to environmental decision-making and sustainability analysis – be this on the side of optimization, simulation, modelling, computational solution procedures, visual analytics, and/or information technologies

The Method of Oilfield Development Risk Forecasting and Early Warning Using Revised Bayesian Network

Oilfield development aiming at crude oil production is an extremely complex process, which involves many uncertain risk factors affecting oil output. Thus, risk prediction and early warning about oilfield development may insure operating and managing oilfields efficiently to meet the oil production plan of the country and sustainable development of oilfields. However, scholars and practitioners in the all world are seldom concerned with the risk problem of oilfield block development. The early warning index system of blocks development which includes the monitoring index and planning index was refined and formulated on the basis of researching and analyzing the theory of risk forecasting and early warning as well as the oilfield development. Based on the indexes of warning situation predicted by neural network, the method dividing the interval of warning degrees was presented by “3σ” rule; and a new method about forecasting and early warning of risk was proposed by introducing neural network to Bayesian networks. Case study shows that the results obtained in this paper are right and helpful to the management of oilfield development risk

Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems

The reduction of greenhouse gas emissions is a major governmental goal worldwide. The main target, hopefully by 2050, is to move away from fossil fuels in the electricity sector and then switch to clean power to fuel transportation, buildings and industry. This book discusses important issues in the expanding field of wind farm modeling and simulation as well as the optimization of hybrid and micro-grid systems. Section I deals with modeling and simulation of wind farms for efficient, reliable and cost-effective optimal solutions. Section II tackles the optimization of hybrid wind/PV and renewable energy-based smart micro-grid systems

Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems

The reduction of greenhouse gas emissions is a major governmental goal worldwide. The main target, hopefully by 2050, is to move away from fossil fuels in the electricity sector and then switch to clean power to fuel transportation, buildings and industry. This book discusses important issues in the expanding field of wind farm modeling and simulation as well as the optimization of hybrid and micro-grid systems. Section I deals with modeling and simulation of wind farms for efficient, reliable and cost-effective optimal solutions. Section II tackles the optimization of hybrid wind/PV and renewable energy-based smart micro-grid systems

Risk Management for the Future

A large part of academic literature, business literature as well as practices in real life are resting on the assumption that uncertainty and risk does not exist. We all know that this is not true, yet, a whole variety of methods, tools and practices are not attuned to the fact that the future is uncertain and that risks are all around us. However, despite risk management entering the agenda some decades ago, it has introduced risks on its own as illustrated by the financial crisis. Here is a book that goes beyond risk management as it is today and tries to discuss what needs to be improved further. The book also offers some cases

Water Resources Management and Modeling

Hydrology is the science that deals with the processes governing the depletion and replenishment of water resources of the earth's land areas. The purpose of this book is to put together recent developments on hydrology and water resources engineering. First section covers surface water modeling and second section deals with groundwater modeling. The aim of this book is to focus attention on the management of surface water and groundwater resources. Meeting the challenges and the impact of climate change on water resources is also discussed in the book. Most chapters give insights into the interpretation of field information, development of models, the use of computational models based on analytical and numerical techniques, assessment of model performance and the use of these models for predictive purposes. It is written for the practicing professionals and students, mathematical modelers, hydrogeologists and water resources specialists

Modeling, Simulation and Optimization of Wind Farms and Hybrid Systems

The reduction of greenhouse gas emissions is a major governmental goal worldwide. The main target, hopefully by 2050, is to move away from fossil fuels in the electricity sector and then switch to clean power to fuel transportation, buildings and industry. This book discusses important issues in the expanding field of wind farm modeling and simulation as well as the optimization of hybrid and micro-grid systems. Section I deals with modeling and simulation of wind farms for efficient, reliable and cost-effective optimal solutions. Section II tackles the optimization of hybrid wind/PV and renewable energy-based smart micro-grid systems