Brad A. Finney

Environmental systems engineering and economics

1. Introduction to Environmental Systems Engineering.- 1.1 Introduction.- 1.2 Systems Engineering.- 1.3 Mathematical Models.- 1.4 The Systems Engineering Problem.- 1.5 Systems Engineering Problems.- 1.6 The Model Building Process.- 1.7 Simulation Modeling.- 1.8 Optimization Modeling.- References.- Problems.- 2. An Introduction to Optimization Theory.- 2.1 Introduction.- 2.2 Classification of Optimization Models.- 2.3 Geometry of the Mathematical Optimization Problem.- 2.4 Nonlinear Optimization and Types of Maxima.- 2.5 Convex Sets and Functions.- 2.6 Weierstrass Theorems.- 2.7 The Local-Global Theorem.- 2.8 The Kuhn-Tucker Conditions.- 2.9 The Kuhn-Tucker Theorem.- 2.10 Interpretation of the Lagrange Multipliers.- 2.11 The Saddle Point Problem.- 2.12 Maximin Dual Problem.- References.- Problems.- 3. Microeconomics: Theory of Production.- 3.1 Introduction.- 3.2 The Competitive Economy.- 3.3 The Production Function.- 3.4 Theory of the Firm.- 3.5 Maximum Output Model.- 3.6 Production Optimization.- 3.6.1 Cost Functions.- 3.6.2 Externalities.- 3.7 Comparative Statics of the Firm.- 3.8 Public Project Evaluation.- 3.9 Case Study 1- Agricultural Benefits.- 3.9.1 Introduction.- 3.9.2 Economic Model.- 3.9.3 Model Results and Conclusions.- References.- Problems.- 4. Microeconomics: Theory of the Household.- 4.1 Introduction.- 4.2 Commodity Space and Preference Relations.- 4.3 Theory of the Household.- 4.4 Comparative Statics.- 4.5 General Equilibrium.- 4.5.1 Classical General Equilibrium.- 4.5.2 Neoclassical General Equilibrium.- 4.6 Market Equilibrium.- References.- Problems.- 5. Engineering Economics.- 5.1 Introduction.- 5.2 The Time Value of Money.- 5.3 Engineering Economic Formulas.- 5.4 Evaluation of Alternatives.- 5.5 Present Worth Method.- 5.6 Annual Worth Method.- 5.7 Benefit Cost Ratio.- 5.8 Internal Rate of Return.- 5.9 Depreciation and Income Tax Analysis.- 5.9.1 Depreciation Methods.- 5.9.2 Pre-1982 Depreciation Methods.- 5.10 Inflation.- References.- Problems.- 6. Linear Programming.- 6.1 Introduction.- 6.2 Optimality of Linear Programming Problems.- 6.3 Standard Form.- 6.4 Basic and Basic Feasible Solutions.- 6.5 The Simplex Algorithm.- 6.6 The Simplex Tableau.- 6.7 The Two-Phase Method.- 6.8 General Summary of the Simplex Algorithm.- 6.9 Duality.- 6.10 Matrix Representation of the Simplex Method.- 6.11 Economic Interpretation of the Dual Problem.- 6.12 The Revised Simplex Method.- 6.13 Sensitivity Analysis.- 6.13.1 Changes in the Cost Coefficients.- 6.13.2 Changes in the Coefficient Matr.- 6.13.3 Changes in the Constant Vector.- 6.14 Large-Scale Linear Programming Models.- 6.14.1 Commercial Codes.- 6.14.2 Matrix Generators and MPS Data.- 6.14.3 Computational Efficiency.- 6.15 Case Study 1 Groundwater Planning.- 6.15.1 Introduction.- 6.15.2 Groundwater Optimization Model.- 6.15.3 Model Application.- 6.15.4 Conclusions.- References.- Problems.- 7. Nonlinear Programming.- 7.1 Introduction.- 7.2 Unconstrained Optimization Methods.- 7.2.1 Indirect Methods.- 7.2.2 Direct Methods.- 7.2.3 Dichotomous Search Method.- 7.2.4 Cauchy Cyclic Coordinate Ascent.- 7.2.5 Powells Conjugate Direction Method.- 7.3 Gradient-Based Methods.- 7.3.1 Cauchys Method.- 7.3.2 Newtons Method.- 7.3.3 Marquardts Algorithm.- 7.4 Constrained Optimization Methods.- 7.4.1 Separable Programming.- 7.4.2 Frank-Wolfe Algorithm.- 7.4.3 Tins Algorithm.- 7.4.4 The Reduced Gradient Method.- 7.4.5 The Generalized Reduced Gradient Method.- 7.4.6 The Convex Simplex Method.- 7.4.7 Boxs Algorithm.- 7.5 Case Study 1 North China Plain Water Management.- 7.5.1 The Management Model.- 7.5.2 Production Functions.- 7.5.3 Model Application.- 7.5.4 Model Results.- 7.5.5 Conclusions.- 7.6 Dynamic Programming.- 7.7 Case Study 2 Water Quality Management.- 7.7.1 Introduction.- 7.7.2 Mathematical Model.- 7.7.3 The Management Model.- 7.7.4 Dynamic Programming Solution.- 7.7.5 Model Application and Results.- 7.7.6 Conclusions.- 7.8 linked Simulation-Optimization Methodology.- 7.9 Case Study 3 Management of Saltwater Intrusion.- 7.9.1 Introduction.- 7.9.2 Management Model.- 7.9.3 Optimization Analysis.- 7.9.4 Conclusions.- 7.10 Case Study 4 Groundwater Remediation.- 7.10.1 Introduction.- 7.10.2 Groundwater Optimization Remediation Model.- 7.10.3 Optimization Results.- 7.10.4 Conclusions.- 7.11 Multiobjective Optimization.- 7.11.1 Kuhn-Tucker Conditions for Nondominated Solutions.- 7.11.2 Weighting Method.- 7.11.3 The Constraint Method.- 7.11.4 Overview of Generating Techniques.- 7.11.5 Goal Programming.- 7.12 Case Study 5 Equity in Water Quality Management.- 7.12.1 Equity Measures.- 7.12.2 Model Application and Results.- 7.12.3 Conclusions.- References.- Problems.- Appendix A. Review of Mathematics.- A.1 Introduction.- A.2 Analysis.- A.3 Vectors and Matrices.- A.4 Matrix Operations.- A.5 Determinants and the Matrix Inverse.- A.6 Quadratic Forms.- A.7 Scalar, Vector, and Matrix Derivatives.- A.8 Directional Derivative.- A.9 Eigenvalues and Eigenvectors.- A.10 Implicit Function Theorem.- A.ll Taylor Series.- A.12 Leibnitzs Rule.- References.- Appendix B. Classical Optimization.- B.1 Introduction.- B.2 The Unconstrained Optimization Problem.- B.3 The Lagrange Multiplier Method.- B.4 The Lagrange Multiplier.- References.

Generation of bivariate solar radiation and temperature time series

Abstract A bivariate periodic time series model for daily sequences of dry bulb air temperature and solar radiation is developed. An autoregressive model is first used to produce a monthly time series. A temporal disaggregation process is then employed to produce the daily series from the monthly series. When applied to an historical time series, the models preserve the first- and second-order moment properties as well as the correlation properties of the monthly and daily historical data. In addition, the skewness coefficient of the observed sequence is reasonably well preserved at both temporal levels. The model can provide multilevel synthetic meteorological data that is important in simulating solar energy systems that consider both short- and long-term performance.

STORMWATER OVERFLOWS: MODELLING IMPACTS ON THE RECEIVING WATERS AND THE TREATMENT PLANT

ABSTRACT The development and application of mathematical models for analysing the response of receiving water quality to stormwater overflows is reviewed. As a basis for the review three types of analysis are distinguished: (i) characterisation of the summary statistics of events at discrete points in time; (ii) quantification of the longer-duration (steady-state) consequences after the passage of an event; and (iii) description of the temporal (dynamic)variation of the stream response both within and between events. Special reference is made to models of the mechanisms of transport and mixing and to the conceptual inter-relationships among the classical advection-dispersion, continuously stirred tank reactor (CSTR), and aggregated dead-zone (ADZ) representations of these mechanisms. The status of treatment plant modelling is reviewed very briefly, and results for a hypothetical simulation study of storm retention tank performance are presented. Possible future directions for the subject are outlined including, inter alia: the need for detailed experimental studies of sediment transport and sediment-water interactions; a hierarchical approach to problem-solving and model development; solutions to the problems of model calibration and the assessment of uncertainty; and models for use in real-time estimation and forecasting schemes.

Stochastic analysis of estuarine hydraulics

A new methodology is presented for the solution of the stochastic hydraulic equations characterizing steady, one-dimensional estuarine flow. The methodology is predicated on quasi-linearization, perturbation methods, and the finite difference approximation of the stochastic differential operators. Assuming Mannings roughness coefficient is the principal source of uncertainty in the model, stochastic equations are presented for the water depths and flow rates in the estuarine system. Moment equations are developed for the mean and variance of the water depths. The moment equations are compared with the results of Monte Carlo simulation experiments. The results confirm that for any spatial location in the estuary that (1) as the uncertainty in the channel roughness increases, the uncertainty in mean depth increases, and (2) the predicted mean depth will decrease with increasing uncertainty in Manningsn. The quasi-analytical approach requires significantly less computer time than Monte Carlo simulations and provides explicit

An Introduction to Optimization Theory

Optimization models in environmental systems consist of a set of objectives, constraints, and decision or control variables. The decision variables detail the possible operational, planning, or design alternatives. In many problems, decision variables include state variables of the environmental system. The optimization models are predicated on mathematical models describing the underlying flow, mass, or energy transport processes. The mathematical models are used in optimization modeling to relate how the decision variables affect the state variables of the environmental system.

Water Quality Assessment of Submerged Tire-Derived Aggregate Fills

AbstractFor many of its intended civil engineering applications, tire-derived aggregate (TDA) comes into contact with water and may leach organic and inorganic compounds. The primary objective of t...

Microeconomics: Theory of Production

Wastewater treatment plant design and the control of regional groundwater quality are examples of environmental systems engineering problems. The design of the unit processes of a wastewater treatment plant is based on the control and transformation of an influent waste flow, a water quality resource. The management of groundwater quality is essentially a resource management problem involving both the water supply and the water quality (the assimilative capacity) of the aquifer system.

Microeconomics: Theory of the Household

Microeconomic models of pure competition represent the producing and consuming elements of the economy by a series of homogeneous firms and households. As we examined in Chapter 3, production and resource (input) decisions can be analyzed with simple economic models of the production process. The optimal solutions of these models generate the output supply and input demand functions of the firms. The supply functions define how much will be produced by the firm at a given market price. The input demand functions relate the unit cost or price of a resource to how much is demanded of that resource.

Introduction to Environmental Systems Engineering

Environmental systems engineering is broadly concerned with the planning and management of water, air, and land resources. Environmental engineering has evolved over the past two decades from traditional, cost minimization design problems to problems typified by conflicting engineering, environmental, and economic objectives and constraints. The evolution of environmental engineering from sanitary engineering and the problems associated with wastewater treatment design has been caused in part by the recognition that environmental problems are multi-disciplinary problems. The evaluation of regional water quality or the conjunctive management of groundwater and surface water resources are environmental planning and management problems-problems involving engineering design, resource economics and environmental analysis. The decision-making in these types of problems involves a hierarchy of engineering activities that range from the design of an individual pumping well in a groundwater basin to controlling reservoir releases to satisfy municipal, industrial, or agricultural water demands. All of these decisions are ultimately based on an understanding of how the environmental system (the groundwater basin or stream or river) responds to, or is affected by the engineering decisions.

TMDLs and the Electric Power Industry: The Challenge of Atmospheric Deposition

The results of a nationwide review of TMDLs (Total Maximum Daily Load) are presented that identify the regional distribution and variation of TMDLs, the target parameters addressed, the potential impacts of TMDLs on the electric power industry, and the different approaches that have been taken by various parties to develop TMDLs. The paper reviews TMDLs that have been developed for sources of pollutants (such as mercury) from atmospheric deposition, highlighting the difficulties in developing such TMDLs and the impact of these TMDLs on the Electric Power Industry.