A wide range of optimisation methodologies exist for solving optimal control trajectory problems. With most approaches it is necessary to solve iteratively, starting from an initialising solution (often referred to as an initial guess). In this paper we investigate the performance of two different dynamic optimisation strategies for batch beer fermentation temperature control, using different initialisation profiles. A sequential method, Control Vector Parametrisation (CVP) is demonstrated as unable to produce solution profiles satisfying the industrially imposed undesirable by-product species concentration constraints. An alternative simultaneous method, Complete Parameterisation (CP), is employed in order to determine solutions satisfying these essential industrial production constraints. Blind initialisation guesses (isothermal profiles) have been shown to produce solution profiles not suitable for implementation on real fermentors; more promising candidate profile initialisations yield superior solutions. The use of a state-of-art NLP solver (IPOPT) with analytical first derivatives achieves remarkable solution robustness, eliminating initialisation and discretisation level dependence.