By linear regression analysis, the coefficients (β) can be estimated. It often turns out that some of the effects (usually second-order terms) are statistically insignificant and can be excluded from the model by either e.g. step-wise linear regression or a criterion based strategy. The response surface model is optimised using the SLSQP algorithm in order to find process parameters and operating conditions for optimial grey methanol production for comparison with previous pubulished data. The other approach taken in the current study is using a Kriging surogate model for optimisation. The sampling is generated as an optimized Latin-Hypercube design by the pyKriging package. It is suggested that for up to 10 variables, a sampling size of 10-15