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Editor’s message: computer simulation and scaling-up

Computer simulation is a useful tool supporting design of industrial plants, seeking to make efficient scale-ups. There are good modelling and simulation papers about microwave processing that deal with statements such as volumetric heating, reduced processing time and energy saving. Among them, there are some that are careful with the mathematical treatment, but rest on simulated results. The great number of published papers reporting results based on computer simulations rather than from actual experiments, without comments regarding their validation, is an indicator of the confidence of researchers and engineers on their reliability. This trustworthiness is also promoted by specialized papers dedicated to the value of computer simulation for saving time and other resources while reducing the number of experiments describing the behavior of a system with the proper set of concatenated equations, but it is often overpassed that a perfect match among that description and reality requires a large set, strictly one that contains all involved phenomena variables. This confidence has resulted in many papers that do not show any sort of validation yet, reach to conclusions that are later taken by other as if they were coming from actual experiments. Indeed, there are some that openly present simulations as experimental results. Microwave processing involves several phenomena occurring simultaneously and multi-dependently: dielectric and resistive heating, heat transfer, besides kinetics and mass transport, if other processes than just heating are taking place. Therefore, although simulations at laboratory scale are possible, extrapolating to scale-up, where relationship masspower and size-power are the most important differences among laboratory scale and industrial scale, is difficult without a sort of experimentation. It is well known that the weight of each phenomenon on the behavior of a system must be considered for assuring that the most important contributions are included in the set that conforms the theoretical model. Since every considered phenomenon implies an equation, a process of simplification is necessary to have a set of equations that is manageable. This task could begin by assuming that some phenomena have negligible effects on the process, that boundary conditions are known at all places, accept geometrical simplifications and constant properties. There are other sources of error that are not part of the performed simplification, such as the accuracy of the numerical methods or the capacity of the computer and software to deal with large numbers. The practical implementation of the set of equations to show the outcome of the model is the simulation, which could be presented as virtual instrumentation or other visual methods, even those that resemble the ‘real world’ system. Most commonly, the models are validated with tests conducted in the laboratory where the conditions are more in compliance with the assumptions. However, it is impossible to take all the phenomena into a model and there always will be constants that are left out, and another aspect that must be considered is that constant conditions usually employed for validating model always present statistical variations. That is the reason for having