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Table of contents
Chapter I. General Introduction
I.1. Wastewater as a pollution stream
I.2. Wastewater as a resource recovery opportunity
I.2.1. Extracting resources from wastewater
I.2.2. Urine source separation
I.3. Quantifying benefits: Wastewater treatment modelling and simulation
I.3.1. Obtaining data: Influent generation
I.4. Evaluating benefits: Life Cycle Assessment (LCA)
I.5. Improving systems: Multi-objective optimization
I.6. Research objectives and tasks
I.6.1. Thesis outline
Chapter II. Coupling Dynamic Modelling and LCA
Entitled of the paper: Evaluation of new alternatives in wastewater treatment plants based on Dynamic Modelling and Life Cycle Assessment (DM-LCA)
Abstract
II.1. Introduction
II.2. Materials and methods
II.2.1. The integrated DM-LCA methodology
II.2.2. Plant layout and scenarios
II.2.3. LCA
II.2.3.1. Goal & scope
II.2.3.2. Life cycle inventory
II.2.3.3. LCIA
II.3. Results and discussion
II.3.1. Reference scenario
II.3.2. Results of alternative scenarios: nutrient recovery, efficiency and energy consumption
II.3.3. Results of alternative scenarios: environmental impacts
II.3.3.1. Endpoint impacts
II.3.3.2. Midpoint results
II.4. Conclusion
Chapter III. Influent Generator
Entitled of the paper: A dynamic influent generator to account for alternative wastewater management: the case of urine source separation
Abstract
III.1. Introduction
III.2. General overview
III.3. Flow generation
III.4. Pollutants generation
III.4.1. General aspects
III.4.2. Composite variables
III.4.3. Fractionation into state variables
III.5. Noise addition
III.6. Example of simulations obtained with the generated influent
III.7. Discussion
III.7.1. Average results
III.7.2. Daily and weekly profiles
III.8. Use of the generated influent for process simulation
III.8.1. Comparison between different models
III.8.2. Effect of urine retention levels
III.9. Conclusions
Chapter IV. Feasibility of Multi-Objective Optimization
Entitled of the paper: Feasibility of rigorous multi-objective optimization of wastewater management and treatment plants
Abstract
IV.1. Introduction
IV.2. Materials and methods
IV.2.1. Dynamic Modelling (DM) approach
IV.2.2. Life Cycle Assessment (LCA) approach
IV.2.3. Efficient Multi-Objective Optimization (EMOO)
IV.2.3.1. Problem formulation: objective functions, constraints and range of decision variables
IV.2.3.2. Expensive optimization algorithm and general settings
IV.2.4. Integrated framework: DM-LCA-EMOO
IV.3. Results and discussion
IV.3.1. WWTP optimization and objectives’ dependencies
IV.3.2. Drivers for an optimal treatment
IV.3.3. Problem formulation and computational feasibility of multi-objective optimization
IV.4. Conclusion
Chapter V. Case Studies on Multi-Objective Optimization
Entitled of the paper: LCA based multi-objective optimization of conventional and innovative wastewater management and treatment scenarios
Abstract
V.1. Introduction
V.2. Materials and methods
V.2.1. Dynamic Modelling – Life Cycle Assessment – Efficient Multi-Objective Optimization (DM-LCA-EMOO) coupling approach
V.2.1.1. Dynamic modelling (DM)
V.2.1.2. Life Cycle Assessment (LCA)
V.2.1.3. Efficient Multi-Objective Optimization (EMOO)
V.2.2. WWTP scenarios (conventional vs. innovative)
V.2.3. Optimization problem formulation
V.2.3.1. Objective function
V.2.3.2. Decision variables and constraints handling
V.2.3.3. Recall on optimization problem formulation
V.3. Results and discussion
V.3.1. Reference scenario
V.3.1.1. General results
V.3.1.2. Analysis on decision variables
V.3.1.3. Steady state versus dynamic modelling approach
V.3.1.4. Total Endpoint versus Midpoint Global Warming Potential
V.3.2. Alternative scenario (ANA)
V.3.3. Consequences on energy autarky
V.3.4. Benefits achieved from REF and ANA scenarios
V.4. Conclusions
Chapter VI. Conclusions and perspectives
References
