How can you use sensitivity analysis to improve process economics?

1 What is sensitivity analysis?

Sensitivity analysis is a method of assessing how the uncertainty or variability of one or more input variables affects the output or outcome of a model or system. In chemical engineering, sensitivity analysis can be applied to process design, simulation, optimization, and evaluation. By changing the values of the input variables within a reasonable range, you can observe how the output variable, such as NPV or IRR, responds and determine the most influential factors and their interactions.

2 How to perform sensitivity analysis?

Different techniques and tools are used for performing sensitivity analysis, depending on the complexity and purpose of the model. One-at-a-time (OAT) involves varying one input variable at a time and measuring its impact on the output, while Monte Carlo (MC) randomly samples the input variables from their probability distributions. Design of experiments (DOE) is a more efficient and effective way to explore the input space and identify the main and interaction effects of the input variables. OAT is simple and intuitive, but does not capture interactions or synergies among variables. MC is more robust and comprehensive, but requires a large number of simulations and computational resources. DOE requires a proper design and analysis of the experiments.

3 How to interpret sensitivity analysis results?

The results of sensitivity analysis can be presented and interpreted in various ways, such as a tornado diagram, a scatter plot, or a Pareto chart. A tornado diagram is a graphical representation of the OAT method that ranks input variables by their impact on the output variable, while a scatter plot is a graphical representation of the MC method that visualizes the distribution and correlation of the output variable and the input variables. Lastly, a Pareto chart is a graphical representation of the DOE method that identifies significant and insignificant factors and their interactions. Each of these methods offer useful insights into the sensitivity analysis and can help compare the relative importance and sensitivity of the input variables.

4 How to use sensitivity analysis to improve process economics?

Sensitivity analysis can help you to improve the process economics and feasibility by identifying the critical and uncertain input variables that affect the output variable, such as NPV or IRR, and prioritizing them for further analysis or optimization. It can also be used to evaluate the trade-offs and scenarios of different input variables and their levels, so that you can select the optimal combination that maximizes or minimizes the output variable. Additionally, sensitivity analysis can be used to assess the risks and opportunities of the process, allowing you to develop contingency plans or strategies to mitigate or exploit them.

5 What are some examples of sensitivity analysis in chemical engineering?

Sensitivity analysis can be applied to various types of chemical engineering processes and projects, such as biofuel production, carbon capture and storage, and waste management. In the case of biofuel production, it can be used to evaluate the economic viability and environmental impact of different feedstocks, conversion technologies, and product markets. For carbon capture and storage, it can assess the technical and economic feasibility and performance of different technologies. Furthermore, it can compare and optimize the economic and environmental outcomes of different waste management strategies and technologies. Ultimately, sensitivity analysis can help identify the key factors and uncertainties that influence the cost and benefit of a process, as well as determine the optimal waste reduction, reuse, and recycling rates.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?