How do you perform sensitivity and uncertainty analysis for reaction engineering and optimization problems?

2 Uncertainty analysis

Uncertainty analysis is the quantification and propagation of the uncertainties in the input parameters or variables of a system to the output or response of the system. It can help you assess the reliability and confidence of the system, estimate the error bounds and intervals, and identify the sources of uncertainty. There are different types of uncertainty, such as aleatory, epistemic, and hybrid, depending on the nature and origin of the uncertainty. Some common methods for uncertainty analysis include interval analysis, fuzzy set theory, Bayesian inference, and evidence theory.

4 Optimization methods

Optimization methods are techniques for finding the optimal or best solution for a system that has one or more objectives and constraints. It can help you maximize or minimize the output or response of the system, such as yield, selectivity, conversion, or profit. There are different types of optimization methods, such as deterministic, stochastic, and hybrid, depending on the nature and structure of the problem. Some common methods for optimization include gradient-based methods, evolutionary algorithms, and multi-objective optimization.

5 Sensitivity-based optimization

Sensitivity-based optimization is a combination of sensitivity analysis and optimization methods that uses the information from the sensitivity analysis to guide the optimization process. It can help you improve the efficiency and effectiveness of the optimization, reduce the computational cost and time, and handle the uncertainties and nonlinearities of the system. There are different types of sensitivity-based optimization, such as gradient-based optimization, sequential quadratic programming, and surrogate-based optimization, depending on the sensitivity measure and optimization algorithm.

6 Uncertainty-based optimization

Uncertainty-based optimization is a combination of uncertainty analysis and optimization methods that incorporates the uncertainties in the input parameters or variables of the system into the optimization process. It can help you find the optimal or robust solution for the system that is less sensitive to the uncertainties, account for the risk and variability of the system, and handle the multiple objectives and constraints of the system. There are different types of uncertainty-based optimization, such as robust optimization, chance-constrained optimization, and reliability-based optimization, depending on the uncertainty measure and optimization criterion.