TRADITIONAL DESIGN USING DESIGN GUIDELINES

TRADITIONAL DESIGN USING DESIGN GUIDELINES

PROPOSED PROBABILISTIC DESIGN METHODOLOGY

This section aims to provide a general outline for the proposed probabilistic design method of WWTPs. Figure 3-1 depicts the proposed methodology which is comprised of three main steps including: 1) Generation of a set of pre-designs with different levels of safety, 2) Preliminary evaluation and screening of pre-designs, 3) Quantification of the probability of non-compliance (PONC) with the effluent standards and total cost. As indicated in Figure 3-1, the first step in the proposed design methodology is the generation of a set of pre-designs generated using a steady state design tool (i.e. a steady state model or a design guideline). The initial sizing of a plant is thus not significantly different from conventional steady state-based design methods in which the proper values are selected for design inputs (e.g. influent flow, COD concentration, SVI, etc) and the size of different treatment units is calculated using a set of equations and/or rules. However, in contrast to the steady state design methods in which single values are selected for design inputs to calculate the size of different treatment units, a range of values reflecting different levels of safety are assigned to design inputs and a set of values is calculated for the different treatment units. Section 3.1 explains the details of the generation of a set of predesigns using a steady state design tool. These pre-designs will be further evaluated in the second part of the proposed probabilistic design methodology as shown in Figure 3-1 (i.e. Preliminary evaluation and screening of pre-designs). After generating a set of pre-designs in step 2), design alternatives that either have a very poor performance in terms of effluent quality or that do not yield a significant difference in the performance under a typical dynamic flow and loading scenario are identified. To identify these alternatives, a two-step screening procedure based on the statistical distribution of the design outputs (i.e. the size of different treatment units) and dynamic simulation of the design alternatives is proposed (details in Section 3.2). This allows selection of a handful of design alternatives for which PONC will be calculated in the third step of the proposed probabilistic design method.
The third step of the proposed probabilistic design method constitutes procedures used for the model-based calculation of PONC considering the variability of influent time series and the parametric uncertainty in the dynamic model of each of the selected design alternatives. Synthetic generation of dynamic influent time series, uncertainty characterization and random generation of model parameters, different methods for propagating the effect of influent variability and model parameter uncertainty, and in the end calculation of PONC and the total cost for the selected design alternatives are covered in Section 3.3.
The details of the proposed probabilistic design method are explained in this chapter. Chapter 4 focuses on the influent generation. The application of the proposed design method to an actual case study and its corresponding results can be found in Chapter CHAPTER 5:.

Generating a set of steady state pre-designs with different levels of safety

The initial sizing of a plant is usually made under steady state conditions. A commonmethod that is based on the assumptions of steady state conditions is the application of design guidelines. To make a design using a design guideline, proper values are selected for design inputs and then the size of the different treatment units are calculated using the equations and rules that are available in that design guideline. Alternatively one can use a steady state model to calculate the dimensions of a plant, if the required design inputs have been provided. The types of design inputs and outputs required to make a steady state design as well as to generate a set of pre-designs with different levels of safety are covered in this section.

Le rapport de stage ou le pfe est un document d’analyse, de synthèse et d’évaluation de votre apprentissage, c’est pour cela rapport gratuit propose le téléchargement des modèles gratuits de projet de fin d’étude, rapport de stage, mémoire, pfe, thèse, pour connaître la méthodologie ?avoir et savoir comment construire les parties d’un projet de fin d’étude.

Table des matières

CHAPTER 1: INTRODUCTION
CHAPTER 2: LITERATURE REVIEW
2.1 TRADITIONAL DESIGN USING DESIGN GUIDELINES
2.1.1 ATV-DVWK-A 131E design guideline
2.1.2 Metcalf & Eddy design guideline
2.1.3 WRC design guideline
2.1.4 Ten States Standards-recommended standards for wastewater facilities
2.1.5 Summary of design guidelines
2.2 MODEL-BASED DESIGN AND OPTIMIZATION
2.3 ROBUST OPTIMAL DESIGN
2.4 PROBABILITY-BASED DESIGN
2.4.1 Definition of the PONC based on the load-resistance concept
2.4.2 Types and characterization of uncertainty
2.4.3 Analytical methods for estimating the PONC
2.4.4 Monte Carlo Method for estimating the PONC
2.5 COST ANALYSIS
2.6 PROBLEM STATEMENT
2.7 OBJECTIVES
CHAPTER 3: PROPOSED PROBABILISTIC DESIGN METHODOLOGY
3.1 GENERATING A SET OF STEADY STATE PRE-DESIGNS WITH DIFFERENT LEVELS OF
SAFETY
3.1.1 Design inputs and outputs using a design guideline and/or steady state model
3.1.2 Monte Carlo simulations for generating a set of pre-designs
3.2 PRELIMINARY EVALUATION AND SCREENING OF PRE-DESIGNS
3.3 QUANTIFICATION OF PONC USING MONTE CARLO SIMULATION
3.3.1 Synthetic generation of influent time series
3.3.2 Uncertainty characterization and random generation of WWTP model
parameters
3.3.3 Propagation of uncertainty and variability using Monte Carlo simulation
3.4 OUTPUT ANALYSIS
3.4.1 Convergence test for one-dimensional Monte Carlo simulation
3.4.2 Convergence test for two-dimensional Monte Carlo simulation
3.5 CALCULATION OF PONC AND TOTAL COST OF DESIGN ALTERNATIVES
3.5.1 Calculation of PONC
3.5.2 Calculation of total cost
CHAPTER 4: INFLUENT GENERATOR FOR PROBABILISTIC MODELING OF NUTRIENT REMOVAL WASTEWATER TREATMENT PLANTS.
4.1 INTRODUCTION
4.2 METHODOLOGY
4.2.1 Weather and air temperature generators
4.2.2 Influent generation in DWF conditions
4.2.3 Influent generation in WWF conditions
4.2.4 Bayesian model calibration of the CITYDRAIN sewer model
4.2.5 Synthetic influent generation
4.3 DATA AND CASE STUDY
4.4 RESULTS AND DISCUSSION
4.4.1 Synthetic generation of rainfall
4.4.2 Synthetic generation of air and bioreactor temperature
4.4.3 Multivariate auto-regressive model for DWF generation
4.4.4 CITYDRAIN model calibration and synthetic influent generation
4.4.5 Synthetic generation of influent time series
4.5 CONCLUSION
CHAPTER 5: APPLICATION OF A NEW PROBABILISTIC DESIGN METHOD FOR WASTEWATER TREATMENT PLANTS-CASE STUDY: EINDHOVEN WWTP
5.1 INTRODUCTION
5.2 PROPOSED PROBABILISTIC DESIGN METHOD
5.2.1 Steady state pre-designs with different levels of safety
5.2.2 Preliminary Evaluation of and screening of design alternatives
5.2.3 Quantification of PONC and Total Cost
5.2.4 Data and case study
5.3 RESULTS
5.3.1 Steady state pre-designs with different levels of safety
5.3.2 Preliminary evaluation and screening of pre-designs
5.3.3 Quantification of PONC and the total cost for the selected designs
5.4 CONCLUSION AND RECOMMENDATIONS
CHAPTER 6: GENERAL CONCLUSIONS AND PERSPECTIVES
6.1 CONCLUSIONS
6.1.1 A practical framework for the probabilistic design of WWTPs
6.1.2 Development of an influent generator using the basic characteristics of sewershed and climate data
6.1.3 Rigorous calculation and communication of PONC concept
6.1.4 Application of the proposed methodology to a real case study
6.2 PERSPECTIVES
6.2.1 Calculation of conditional PONC in view of future anthropological and climate change
6.2.2 Including the uncertainty in the performance of technical components of WWTPs
6.2.3 Devising improved control strategies to reduce the risk of non-compliance
REFERENCES
APPENDIX

Rapport PFE, mémoire et thèse PDFTélécharger le rapport complet

Télécharger aussi :

Laisser un commentaire

Votre adresse e-mail ne sera pas publiée. Les champs obligatoires sont indiqués avec *