Analyzing Risk in Wastewater Process Design: Using Monte Carlo Simulation to Move Beyond Conventional Design Methods
Because there are many variables and uncertainties that can affect the performance of wastewater processes, conventional design methods, which analyze uncertainties in relative isolation, do not always provide an adequate analysis of the risks in a design, including their probability and sensitivity to other variables. A more robust risk analysis method for complex systems is modeling using a Monte Carlo simulator, which allows you to define design variables as probability distributions and generate multiple simulations with different sets of input variables.
The Monte Carlo simulator produces a probability distribution for each outcome and the sensitivity of the outcome to the input variables. To achieve the best results, the Monte Carlo simulator should be used with a robust whole plant process simulator. Combining the two models allows you to evaluate risks throughout a wastewater treatment plant based on uncertainties in raw sewage characteristics, plant performance, and operational setpoints.
MINIMIZING RISK: CONVENTIONAL DESIGN VS. RISK-BASED DESIGN
Process engineers typically attempt to minimize risks by identifying the uncertainties that affect the design (e.g., influent flows and loads, unit process performance), and then designing for a set of conditions that minimize the risk of failure. For example, you define a basic design condition such as a maximum month flow and load, model the plant under those conditions, and size the facilities to prevent failure under those conditions. This approach may lead to an overly conservative design with risks that are difficult to quantify due to the interactions of multiple uncertainties.
Using a risk-based approach to wastewater process design, uncertainties are identified, analyzed, and accounted for during process modeling. The results of the risk-based approach are unit sizing criteria, and an analysis of the risks of selected design decisions.