Noise is simulated by applying a random vector to x, drawn from a normal distribution with variance α. We consider optimisation under three conditions: zero noise (e.g., clean data with α = 0), small noise (α = 0.05), and high noise (α = 0.2). In the zero-noise and small noise cases, Bayesian optimisation with the lower confidence bound acquisition function can reach the global minimum in under 75 learning cycles. However, for the high noise experiment, the optimisation failed to find the global minimum within 75 cycles. In our example, adjacent local minima are relatively close to the global minimum and illustrate the utility of Bayesian optimisation in chemical experiments where often we are primarily interested in an "acceptable" solution. Gaussian process regression can handle unbiased noise by giving an estimate value of its standard deviation. However, it is difficult to determine the range of noise in real experiments and it may be biased due to environmental factors.