**1. Introduction**

For thousands of years, micro-organisms have been spontaneously used in human food preparation. However, scientists did not initiate studying these living beings until the appearance of the microscope in 1680. Among microorganisms widely studied and used in diverse biotechnological applications, the yeast *S. cerevisiae* was mentioned [1–3]. This yeast species was known formerly for its particular exploitation in the production of wine, beer and bread. Recently, it has been used as a "cell factory", able to synthesize a large spectrum of bioactive molecules as recombinant proteins, antibiotics and bioethanol [4–7]. Algeria records a remarkable lack in beet and cane molasses production, indeed, it imports about 18,000 and 13,000 tons per year of each one, respectively, and also imports the yeast strain *S. cervisiae* used as a baker's leaven [8]. However, Algeria has an enormous potential of dates [9,10]. In addition, the production of *S. cerevisiae* biomass from a low quality date variety could

constitute an economic carbon source, especially considering that the production of dates is a bountiful in Algeria. The main objective of the present work is to study the optimization of *S. cerevisiae* biomass production, using date extract as a sole carbon source.

The traditional technique used for optimizing a multivariable fermentation process is difficult and does not take the alternative effects between components into consideration [11,12]. Recently, many statistical experimental design methods have been employed in bioprocess optimization [13–16]. Among these, the central composite experimental design (CCD) is the most suitable for identifying the individual variables to optimize a multivariable system [17,18]. This method was used to optimize many fermentation process, such as acids, antibiotics, enzymes, biomass and ethanol production by several micro-organisms types [19–22]. Furthermore, it was used in design, analysis, and in unit operations. The advantages of this method are the reduction of the number of experiments, reagents, time, financial input and energy [23]. The present work was conducted following these steps: (a) selecting the optimum conditions of three parameters (temperature; initial pH, and sugar concentration extracted from dates) to obtain a high yield of *S. cerevisiae* cells growth using a surface response methodology; (b) exploit the date extract as the sole carbon source for the production of *S. cerevisiae* at optimized conditions; (c) predict the biomass production process by unstructured kinetic models. ff ‐
