Part 5: Addressing Specific Sources of Uncertainty and Variability in Design Practice🔍 Overview- 📐 **Design Guidelines and Safety Factors:...
Published on by Hossein Ataei Far, Deputy Manager of the Research, Technology Development, and Industry Relations Center at NWWEC
🔍 Overview
- 📐 **Design Guidelines and Safety Factors: ** Design guidelines address uncertainties by incorporating safety factors.
- 🎯 **Objective: ** The goal is to identify and prioritize uncertainties to make informed decisions.
- 🔄 **Reducing Uncertainties: ** As the design process advances, uncertainties decrease, which narrows down design options.
🌊 Addressing Variability and Uncertainty in Flow and Load Determination
1. **📜 Historical Data for Design: **
- **Usage of Historical Data: ** Utilize historical data such as facility flow records, population growth trends, and zoning information.
- 📊 **Data Sets: ** Employing multiple data sets can reduce uncertainty.
- 🏗️ **Incorporate Zoning and Improvements: ** Factor in zoning changes and planned capital improvements in preliminary designs.
- 📈 **Saftey Factors: ** Use safety factors to account for variability in flow and concentrations, ensuring performance under peak conditions.
2. **🚰 Per Capita Flows and Loads: **
- **Industry Standards: ** Use industry-accepted per capita flows and loads to verify facility design criteria.
- 🔍 **Re-evaluation: ** Significant discrepancies in data warrant a re-evaluation of the design criteria.
3. **📊 Screening of Influent Wastewater Data: **
- **Historical Data Analysis: ** Analyze historical influent wastewater data for accurate characterization.
- 🔍 **Evaluation Techniques: ** Various techniques are used for data evaluation, though no standard practice exists.
4- The keynote compared two methods for wastewater treatment plant (WWTP) dimensioning: a traditional guideline-based approach 📘 and a mechanistic model-based approach 🧩. The main points are:
4-1. **Uncertainty Analysis**: Both methods are affected by uncertainties in influent concentrations, making uncertainty analysis crucial ⚠️.
4-2. **Model-Based Approach**: This approach simplifies and speeds up uncertainty and sensitivity analyses ⏱️ compared to the traditional method, which involves manual and iterative equation solving 📝.
4-3. **Conventional Approach**: Offers straightforward identification of design limitations 🔍 but is more time-consuming and less efficient ⏳.
4-4. **Future Research**: Needed quantifying interactions for correlated input data and validating mechanistic model outputs 🔬.
Overall, model-based design shows potential advantages in efficiency and handling uncertainties 🌟, while the conventional approach provides clear insights into design constraints [2].
References:
[1] Evangelia Belia and et. al., 2021, IWA
[2] Lindblom, E. U., & Samuelsson, O. (2023). IWA