No abstract

Modeling, simulation, and control of the alcoholic fermentation of grape juice into wine are still not a totally resolved problem. A model that makes it possible to predict alcoholic fermentation development would be a valuable instrument to its technical and economical implications. Considering the field of bioprocess used in food production at the industrial scale, the chapter will be centered on models applicable to oenology. On the first part, the chapter proposes the different approaches that have been taken: "knowledge-based" models, non-physiological mathematical descriptions, behavior prediction models, and empirical models. The second part will deal with a nonlinear model for white wine alcoholic fermentation process which, besides the detailed kinetic model, involves equations corresponding to the physiological phases of yeast cells, the inhibitory effect of ethanol, heat transfer equations, and the dependence of kinetic parameters on temperature.

Based on a nonlinear model, this article realizes an investigation of dynamic behavior of a batch fermentation process using direct sensitivity analysis (DSA). The used nonlinear mathematical model has a good qualitative and quantitative description of the alcoholic fermentation process. This model has been discussed and validated by authors in other studies. The DSA of dynamic model has been permitted to calculate the matrix of the sensitivity functions in order to determine the influence of the small deviations of initial state, control inputs, and parameters from the ideal nominal values on the state trajectory and system output in time. Process optimization and advanced control strategies can be developed based on this work.

One goal of specialists in food processing is to increase production efficiency in accordance with sustainability by optimising the consumption of raw food materials, water, and energy. One way to achieve this purpose is to develop new methods for process monitoring and control. In the winemaking industry, there is a lack of procedures regarding the common work based on knowledge acquisition and intelligent control. In the present article, we developed and tested a knowledge-based system for the alcoholic fermentation process of white winemaking while considering the main phases: the latent phase, exponential growth phase, and decay phase. The automatic control of the white wine’s alcoholic fermentation process was designed as a system on three levels. Level zero represents the measurement and adjustment loops of the bioreactor. At the first level of control, the three phases of the process are detected functions of the characteristics of the fermentation medium (the initial substrate concentration, the nitrogen assimilable content, and the initial concentration of biomass) and, thus, functions on the phase’s duration. The second level achieves the sequence supervision of the process (the operation sequence of a fermentation batch) and transforms the process into a continuous one. This control level ensures the quality of the process as well as its diagnosis. This software application can be extended to the industrial scale and can be improved by using further artificial intelligence techniques.

This paper is based on the modernization of work processes in agriculture by ensuring the efficient management of land and equipment and the acquisition of inputs given the specific natural variation in environmental conditions. Specifically, the paper highlights research from a dual perspective, descriptive and explanatory, according to the methodology of the case study conducted in the field of the agricultural enterprise SC AgriConsorțium SRL, located in the S–W of Romania, by adopting the spatial technology for the aerial monitorization of agricultural crops and for signalizing, in real time, the changes and vulnerabilities of the agroecosystem in order to function and develop sustainably. The research aims to promote spatial technologies to monitor crop growth resources, crop vegetation conditions, the real-time signaling of changes, and vulnerabilities in the agroecosystem. The research study’s results highlight the role of the aerial monitoring of crops and rapid signaling of changes in the agroecosystem, such as vegetation conditions, plant density, quality of applied work, and the destruction of crops by overgrazing for the rapid and relevant assessment of affected areas and damage. The case study of the paper is a modern, innovative, and sustainable tool for digitizing agricultural enterprises to obtain accurate information on changes in the agroecosystem and to adopt a geographical information system for recording and managing data specific to cultivated areas and their use in providing studies and reports necessary for state institutions, respectively, in order to support and guide the decision-making process. The obtained results are the basis for future research on the interpretation and use of information obtained by drones.