Optimal Control Applied to Oenological Management of Red Wine Fermentative Macerations

Abstract

The management of wineries for industrial red winemaking is limited by the capacity and availability of fermentation tanks over the harvest season. The winemakers aim to optimize the wine quality, the fermentative maceration length, and the fermentation tank’s productive cycle simultaneously. Maceration in varietal wine production is carried out until a specific sugar content (digging-out point) is attained, finishing before alcoholic fermentation. Winemakers have found that by trial and error handling of the digging-out point, they can improve the winery capacity and production cost. In this work, we develop an optimal control problem for managing the digging-out point considering two objectives associated with process efficiency and costs. A good compromise between these objectives was found by applying multi-criteria decision-making (MCDM) techniques and the knee point. Two control strategies were compared: free nutrition and traditional nutrition. TOPSIS and LINMAP algorithms were used to choose the most suitable strategy that coincided with the knee point. The preferred option was nitrogen addition only at the beginning of fermentation (6.6–10.6 g/hL of DAP) and a high fermentation temperature (30 °C), yielding the desired digging-out point with a small error (6–9 g/L).

Advancing Wine Fermentation: Extended Kalman Filter for Early Fault Detection

“…For example, in industrial wineries it is common to add nitrogen a second time to ensure a successful process in terms of the productivity and quality. This decision is carried out typically when the density reaches the range of 1080-1050 kg /m 3 that coincides with the time range of the first alarm generated (Luna et al, 2021). As can be seen in figures 6 and 7, the prediction error decreases significantly before reaching 1080 kg /m 3 which means that the information is reliable enough to be used when deciding how much nutrient should be added.…”

“…For the purpose of this paper, which focuses on stuck and sluggish fermentations, a fault is defined as a sugar concentration at t f above a desired value. For example, a fault could be defined as S(t f ) ≥ 2 g /L which corresponds to a dry wine, or could be higher for musts that are drained before the end of alcoholic fermentation, which is common for varietal wines (Luna et al, 2021). With this definition, an alarm for a possible fault could be raised at any time when the prediction is above the threshold.…”

Management tool for oenological decision-making: Modeling and optimization of a hybrid model for fermentative maceration of Cabernet Sauvignon

“…Process cost was quantified as the sum of dynamic diammonium phosphate (DAP) additions throughout fermentation. DAP addition is justified as it is a key source nutrient for yeast growth and fermentation performance, ensuring optimal fermentation outcomes [20].…”

“…Examples of contemporary alternatives include using Genome-Scale Metabolic Models (GEMMs) or expanding traditional dynamic models with secondary aroma metabolism [17,18]. Additionally, models that combine fermentation and extraction kinetics of phenolic compounds during maceration have exhibited promise in large-scale red wine fermentation operations [10,19,20].…”

“…Nonetheless, optimizing the control of alcoholic fermentation in winemaking presents a formidable challenge. The control parameters of both processes, fermentation, and maceration, differ, necessitating the identification of compatible conditions for managing them simultaneously [9,20]. Commercial winemaking aims to optimize factors such as sugar exhaustion, fermentation duration, and the energy needed to regulate fermentation temperature, which can be difficult to quantify [4,9,17,21,22].…”