The food and drink industry is gradually gaining sustainability pressure due to the high carbon emissions rate and limited availability of traditional energy supplies. Thus, it is essential to consider energy efficiency and decarbonisation of the ever-expanding food and drink industry. Ohmic heating technology is a highly energy-efficient and quick method of electrically heating food products. It uses the resistance of the food material to convert electrical energy into heat. However, the heating rate of ohmic heating depends on several process variables, such as electrical conductivity, voltage gradient, and voltage frequency. Therefore, it is important to study and understand the effects of these process variables to optimise the modelling and control of ohmic heating systems to obtain desirable output from food products. Hence, this study presents a review of the effects of process parameters on nutritional and organoleptic qualities, product yields, and energy efficiency of ohmic heating, along with its various industrial applications. The technology shows promising advancements in colour and nutritional quality, increasing energy efficiency, shorter processing times, and higher product yields compared to conventional heating processes.

Ohmic heating is a highly efficient method for rapid fluid heating, with applications in fields such as food processing, pharmaceuticals, and chemical engineering. Prior to its industrial application, thorough analysis and modeling are crucial to ensure safe and efficient operations. Therefore, this research focuses on the development and validation of a transfer function-based model for a continuous flow ohmic heater (CFOH). Validation metrics include root mean square error (RMSE) and mean absolute percentage error (MAPE). The developed model achieves an RMSE of ±1.48 and a MAPE of ±2.58% compared to experimental results, demonstrating its accuracy. Furthermore, the research presents the implementation of robust real-time applications of advanced process controllers, including PID, MPC, and AMPC. These controllers were first simulated using the developed model and subsequently deployed in the pilot plant ohmic heater system to achieve precise temperature control and optimised input voltage. The reliability of this procedure was affirmed through a comparison between simulated results and empirical data obtained from the CFOH pilot plant. The study concludes by suggesting potential applications in fault diagnosis, educational training, system identification, and controller design.

The ascendency of the craft beer movement within the brewing industry may be attributed to its commitment to unique flavours and innovative styles. Mixed-culture fermentation, celebrated for its novel organoleptic profiles, presents a modelling challenge due to its complex microbial dynamics. This study addresses the inherent complexity of modelling mixed-culture beer fermentation while acknowledging the condition monitoring limitations of craft breweries, namely sporadic offline sampling rates and limited available measurement parameters. A data-driven solution is proposed, utilising an Autoregressive Recurrent Neural Network (AR-RNN) to facilitate the production of novel, replicable, mixed-culture fermented beers. This research identifies time from pitch, specific gravity, pH, and fluid temperature as pivotal model parameters that are cost-effective for craft breweries to monitor offline. Notably, the autoregressive RNN fermentation model is generated using high-frequency multivariate data, a departure from intermittent offline measurements. Employing the trained autoregressive RNN framework, we demonstrate its robust forecasting prowess using limited offline input data, emphasising its ability to capture intricate fermentation dynamics. This data-driven approach offers significant advantages, showcasing the model’s accuracy across various fermentation configurations. Moreover, tailoring the design to the craft beer market’s unique demands significantly enhances the model’s practicable predictive capabilities. It empowers nuanced decision-making in real-world mixed-culture beer production. Furthermore, this model lays the groundwork for future studies, highlighting transformative possibilities for cost-effective model-based control systems in the craft beer sector.

Fermentation is a complex biochemical process that transforms brewer’s wort into beer. Beer fermentation is driven by yeast and is influenced by process parameters such as the content of fermentable sugars in wort, temperature, and pH. Traditional methods of modelling this process rely heavily on empirically tuned kinetic models. However, these models tend to be recipe-specific and often require retuning when processes change. This paper proposes a data-driven approach using a Long Short-Term Memory (LSTM) network, a type of recurrent neural network, to model beer fermentation dynamics. By training the LSTM model on real-world fermentation data (1305 fermentations across ales, IPAs, lagers, and mixed-culture beers), including variables such as apparent extract (derived from specific gravity), temperature, and pH, we demonstrate that this technique can accurately predict key fermentation trajectories and support process monitoring and optimisation. When evaluated on representative medoid fermentations as one-step-ahead roll-outs over 0–300 h, the model produces accurate predictions with low errors and minimal residuals. These results show that the LSTM-based model provides accurate and robust predictions across beer styles and operating conditions, offering a practical alternative to traditional mechanistic kinetic models. This work highlights the potential of LSTM networks to enhance our understanding, monitoring, and control of fermentation processes, providing a scalable and efficient tool for both research and industrial applications. The findings suggest that LSTM models can be effectively adapted to model other fermentation processes in beverage production, opening new possibilities for advancing food science and engineering.