Part 4: Addressing Uncertainty and Variability in Design Practice: Insights and Methodologies🔍 Model Application for Design: Key Points1- Cri...
Published on by Hossein Ataei Far, Deputy Manager of the Research, Technology Development, and Industry Relations Center at NWWEC
🔍 Model Application for Design: Key Points
1- Critical Inputs Identification
🎯 Identify Critical Inputs: Designers and engineers need to determine the most critical inputs for their models and select the most appropriate values.
2-Flow, Load, and Model Input Methodology
📏 Methodology Selection: Choose methodologies for determining flows, loads, and other inputs to avoid overly conservative or under-designed systems.
🗣️ Communicate Risk: Communicate the level of risk associated with critical design decisions to stakeholders and decision-makers.
3-Monte Carlo Techniques
💻 Increased Interest: Monte Carlo techniques have gained popularity due to advancements in computer processing power.
🔹Benefits for Treatment Process Design:
📊 Variable Impact Analysis: Analyze the impact of various variables on the design.
🔗 Covariance Consideration: Account for covariance between different variables.
⚙️ Sophisticated Simulations: Use advanced simulators to understand process interactions.
🧩 Assumption Testing: Test a wide range of assumptions and their combinations.
📈 Design Requirements Determination: Determine peak, average, and minimum design requirements.
🔄 Scenario Analysis: Analyze multiple scenarios leveraging computing power.
4- Statistical Analyses in Academia
🧑🏫 Advanced Analyses: Advanced statistical analyses for model calibration and process design are often conducted in academic settings due to the extensive resources and expertise required.
5-Computational Modeling in Wastewater Treatment Processes
🔹Model Types: Various deterministic, stochastic, and time series-based models have been developed to predict WWTP performance.
🔹Mechanistic Models: Predominantly used as predictive models, these incorporate physical and empirical knowledge but may oversimplify reality, leading to model structure uncertainty and ongoing calibration needs.
🔹Data-Driven Models: With increasing data availability, data-driven models are becoming more attractive. They can revolutionize WWTP management by enabling the development of digital twins for near-real-time process simulation.
References:
[1] Evangelia Belia and et. al., 2021, Uncertainty in Wastewater Treatment Design and Operation: Addressing current practices and future directions, IWA Publishing.
[2] Duarte, M. S., Martins, G., Oliveira, P., Fernandes, B., Ferreira, E. C., Alves, M. M., ... & Novais, P. (2023). A review of computational modeling in wastewater treatment processes. ACS Es&t Water, 4(3), 784-804.
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