Predictive modelling of dairy manufacturing processes

A Novel Methodology for Assessing and Modeling Manufacturing Processes

Historically, researchers and practitioners have often failed to consider all the areas, factors, and implications of a process within an integrated manufacturing model. Thus, the aim of this research was to propose a holistic approach to manufacturing processes in order to assess their status and performance to improve target indicators such as product quality. For this purpose, a conceptual model is designed by identifying areas, flows, and indicators that are relevant to the assessment of a manufacturing system. Moreover, using the conceptual model, manufacturing systems can be modeled considering all related flows and decision-making options in the respective areas of production, maintenance, and quality. As a result, this model serves as the basis for the integral management and control of manufacturing systems in digital twin models for the regulation of process stability and quality with maintenance strategies. Thus, an assessment based on the conceptual model improves the knowledge level of all elements involved in the manufacturing of a product according to the desired quality specifications. The continuous monitoring of all areas and flows together with the optimal strategies in the quality and maintenance areas can enable companies to increase their profitability and customer service level. In this context, the discussion section lists key decision aspects for the assessment and improvement of manufacturing systems, while also providing a methodological sequence to evaluate and improve manufacturing systems. In conclusion, the conceptual approach allows better decision making, ensuring continuous optimization along the manufacturing asset lifecycle and providing a unique selling proposition for equipment producers and service engineering suppliers, as well as for production and assembly companies.

Predictive Modeling of Microbial Behavior in Food

Microorganisms can contaminate food, thus causing food spoilage and health risks when the food is consumed. Foods are not sterile; they have a natural flora and a transient flora reflecting their environment. To ensure food is safe, we must destroy these microorganisms or prevent their growth. Recurring hazards due to lapses in the handling, processing, and distribution of foods cannot be solved by obsolete methods and inadequate proposals. They require positive approach and resolution through the pooling of accumulated knowledge. As the industrial domain evolves rapidly and we are faced with pressures to continually improve both products and processes, a considerable competitive advantage can be gained by the introduction of predictive modeling in the food industry. Research and development capital concerns of the industry have been preserved by investigating the plethora of factors able to react on the final product. The presence of microorganisms in foods is critical for the quality of the food. However, microbial behavior is closely related to the properties of food itself such as water activity, pH, storage conditions, temperature, and relative humidity. The effect of these factors together contributing to permitting growth of microorganisms in foods can be predicted by mathematical modeling issued from quantitative studies on microbial populations. The use of predictive models permits us to evaluate shifts in microbial numbers in foods from harvesting to production, thus having a permanent and objective evaluation of the involving parameters. In this vein, predictive microbiology is the study of the microbial behavior in relation to certain environmental conditions, which assure food quality and safety. Microbial responses are evaluated through developed mathematical models, which must be validated for the specific case. As a result, predictive microbiology modeling is a useful tool to be applied for quantitative risk assessment. Herein, we review the predictive models that have been adapted for improvement of the food industry chain through a built virtual prototype of the final product or a process reflecting real-world conditions. It is then expected that predictive models are, nowadays, a useful and valuable tool in research as well as in industrial food conservation processes.