Abstract

Cooking process involves changes in the chemical composition and structure of food by controlled heat addition and an oven is one of the devices widely used for it. A cooking cycle consists of a sequence of phases ( e.g. preheat, cooking mode). The process is very sensitive to temperatures, both in steady and transient phases. So it is very critical to maintain the desired temperature and heating rate inside the oven cavity to achieve the right quality of food. Energy norms are becoming stringent which trigger the need for energy efficient designs and operations. Opportunities in design include exploring and evaluating ideas in structural and material domains. And various possible control strategies are to be studied for improving efficiency during operation. Owing to these needs, using predictive methodology and systems engineering approach has become very crucial. Most conventional ovens have resistive heating elements as heat sources and a fan ( optional) inside the cavity. In the present study a mathematical model is developed for thermal behavior of the oven using a lumped modeling approach. The model is developed using Dymola. It can predict transient temperature profiles at different locations of the oven (like walls, air, door) and energy consumption including decomposition of energy. The results produced by the model are compared with a set of experimental data generated in the lab. The model can support the design phase to analyze effects of different design factors like insulation thicknesses, different wall emissivities, cavity dimensions, heater areas, door structure, material selections etc. on transient thermal behavior of the oven and to generate tradeoffs between them. It can also support design of control algorithms to tune control factors and to compare different control strategies. Development of control algorithms focus on achieving desired temperature profile inside the cavity using minimum energy. So the design ( structure) and control need to interact starting from the initial phase to achieve optimum temperature and energy performance. The existing model can act as a plant (virtual design prototype) in control algorithm development process providing futuristic data to control algorithms.