Traditional batch ethanol fermentation poses the problems of poor production and economic viability because the lag and stationary phase always demand considerable fermentation time; plus, downtime between batches is requested to harvest, clean, and sterilize, decreasing the overall productivity and increasing labor cost. To promote productivity and prolong the production period, avoid process instability, and assure a substantial production of ethanol and a minimal quantity of residual substrate, this paper proposed a nonlinear adaptive control which can realize global stabilizing control of the process starting from batch mode to achieve batch/washout avoidance. Due to the dynamic nature and complexity of the process, novel estimation and control schemes are designed and tested on an ethanol fermentation model. These schemes are global stabilizing control laws including adaptive control to avoid input saturation, nonlinear estimation of the unknown influential concentration through a higher-order sliding mode observer, and state observers and parameter estimators used to estimate the unknown states and kinetics. Since the temperature is an important factor for an efficient operation of the process, a split ranging control framework is also developed. To verify the process performance improvement by continuous fermentation, tests performed via numerical simulations under realistic conditions are presented.