SANITAS Sustainable and Integrated Urban Water System Management - PowerPoint PPT Presentation

Sustainable and Integrated Urban Water System Management SANITAS Sustainable and Integrated Urban Water System Management Qualitative Modelling for Urban Water System Decision Support 4th SANITAS e-Seminar Jose Porro – Universitat de Girona

Sustainable and Integrated Urban Water System Management Agenda SANITAS Overview Intro to Qualitative Modelling Qualitative Risk Models for WWTP Control Benchmarking Qualitative / Mathematical Modelling Framework for Assessing Integrated UWS GHG Emissions Eindhoven Case Concluding Remarks Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management

Sustainable and Integrated Urban Water System Management SANITAS Research Objectives Focus on extending knowledge and filling gaps for practical implementation of sustainable and integrated approaches to UWS managment and policy-making decision support in meeting emerging challenges • Energy • Climate Change • Stricter Water Quality Stds • Emerging Contaminants • Resource Recovery • Efficiency • Minimizing environmental impact while meeting water objectives • Holistic / Integrated solutions Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management Life Cycle Cost Return on Capital Costs Traditional Criteria Investment New Criteria Economic Rate Increases Public GHG emissions Perception Sustainability Social Environmental Noise Political Life Cycle Assessment Employee Health Water Quality Job Creation 5 Laboratory of Chemical and Environmental Engineering

SANITAS Projects and Decision Pathways Sustainable and Integrated Urban Water System Management Decision-Making Garcia (ESR7) Integrated Decision-Making Master Planning Hadjimichael (ESR1) EDSS Meng (ESR4) Catchment- Saagi (ESR7) BSM Based/Real-time Consenting Solon (ESR8) BSM System-wide Plant-wide Rehman (ESR6) Modelling GHG CFD Ricken (ESR5a) Micropollutants Batista (ESR5b) Controlling Snip (ESR9) Modelling GHG, Micropollutants Sulfide,GHG Sewer Porro (ER1) Qualitative Modelling Vallet (ER3) Paulo (ESR3) Modelling Biogas formation CSO WQ River Water Supply & Treatment Castro (ESR10) Arnaldos (ER2) IMS Modelling Modelling GHG Stefani (ESR2) IMS Energy granular sludge anammox Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management Qualitative Modelling Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management Why use Qualitative Modelling? • When phenomena of biological nature cannot be predicted adequately by general and validated deterministic models due to lack of sufficient mechanistic understanding of the underlying kinetics and population dynamics (Comas et al., 2008) • When understanding of complex mechanism is not needed to answer practical questions or provide decision support Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management Qualitative Modelling Techniques Artificial Intelligence (AI) or Knowledge-based systems mimic human perception, learning and reasoning to solve complex problems (Chen et al., 2008) Machine Case-based Rule-based Agent Statistical / Neural learning / reasoning Black reasoning technology Data Mining networks Data mining Box

Sustainable and Integrated Urban Water System Management Differences of AI with Numerical or Deterministic Methods NUMERICAL METHODS ARTIFICIAL INTELLIGENCE Symbolic processes Numerical processes Complex processes “ Normal ” processes Heuristic resolution Mathematical resolution Approximate solutions “ exact ” solutions Approximate information Exact information QUALITATIVE QUANTITATIVE

Sustainable and Integrated Urban Water System Management Rule-based System Development Knowledge Acquisition Knowledge Representation in a graphical way Codification of the branches by means of production rules: IF <conditions> THEN <conclusions> Knowledge Base

Sustainable and Integrated Urban Water System Management Rule-based System: knowledge acquisition Knowledge acquisition : Sources and methods SPECIFIC SPECIFIC data data DATA BASE DATA BASE DATA MINING KNOWLEDGE KNOWLEDGE interviews interviews KB KB experience experience experience EXPERT EXPERT GENERAL GENERAL PROCESS PROCESS theory theory LITERATURE LITERATURE REVIEW KNOWLEDGE KNOWLEDGE time time (Comas, 2012)

Sustainable and Integrated Urban Water System Management Environmental Decision Support Systems (EDSS)  Complex management of environmental systems • advantages but limitations  Single AI techniques could not succeeded • all knowledge could not be captured in one reliable model  Link control algorithms and mathematical models to AI techniques  Environmental Decision Support Systems, which integrate • numerical control • mathematical modelling, • heuristic knowledge (literature, experts), • experiences “ a new tool INTEGRATING different reasoning models (mahemtical, AI, GIS, et.) complementing each other and thus increasign the overall potentialities. This tool helps to reduce the time in which decisions are made, and improves the consistency and quality of those decisions ”

Sustainable and Integrated Urban Water System Management Qualitative Modelling Examples • Poch et al., 2004 – KBS EDSS for WWTP Supervision and Technology Selection • Garrido et al. 2012 – KBS EDSS for WWTP Technology Selection • NOVEDAR_EDSS • Rodriguez-Roda et al., 2002 – Hybrid KBS/CBS DSS for WWTP microbial operational problems • Comas et al., 2008 – Hybrid numerical/KBS for assessing risk of WWTP settling problems of microbial origins Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management Integrating Mathematical and Qualitative Modelling UWS Deterministic Modelling tools have done well to address complexity and dynamics • Sewer models • ASM models • BSM models • River water quality models Build on previous success and extend capabilities and decision support by leveraging Deterministic model output data for Qualitative Assessment • Microbial operational problems • GHG emissions • Integrated models and benchmarking Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management Qualitative WWTP Risk Assessment Models Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management Qualitative AS Risk Model for Assessing Risk of Solids Separation AS Risk Model (Comas et al., 2008) • Knowledge Acquisition from vast heuristic knowledge from experts and literature • Knowledge formalized in Decision Trees • Implemented in fuzzy-logic Rule-Based system Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management AS Risk Model Development AS Bulking Knowledge Representation thru Decision Trees Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management AS / AD Risk Model Implementation in Benchmark Simulation Platforms BSM2 plant configuration (Nopens et al. 2010) Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management AS Risk Model BSM1 Results Open-loop Dry Weather Integrated Overall Risk (Comas et al., 2008) Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management AS Risk Model BSM1 Results Comparison of Three Control Strategies (Comas et al., 2008) Highlights importance of considering operational problem dimension to typical WWTP control strategy benchmarking. Only considering typical cost (OCI) and WQ (EQI), one would be led to high risk conditions of sludge bulking and foaming.

Sustainable and Integrated Urban Water System Management Extending Qualitative AS Risk Model and Concept • AS Risk Model needs validation based on full-scale data • Using digitized microscope photographs • Modelling foaming events • Extend AS Risk Model for other configurations and extend concept for other complex UWS problems (eg. assessing UWS GHG Risk) Porro (UdG) SANITAS ER1 – Qualitative Modelling in UWS Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management Applying AS Risk Model to other Configurations Terrassa WWTP (Terrassa, Spain) approx 7000m 3 /d – MBR train • Measurement campaign to characterize MBR process performance • Model development and calibration • Simulation of various optimization strategies • Adapt and apply AS Risk Model to confirm optimization is not increasing risk of AS bulking / foaming and MBR operational problems • First step in developing generic AS Risk Model for any configuration Laboratory of Chemical and Environmental Engineering

Sustainable and Integrated Urban Water System Management Terrassa MBR Model Laboratory of Chemical and Environmental Engineering