This paper addresses the selection of controlled variables, that is, "what should we control". The concept of self-optimizing control provides a systematic tool for this, and we show how it can be applied to the Tennessee Eastman process, which has a very large number of candidate variables. In this paper, we present a systematic procedure for reducing the number of alternatives. One step is to eliminate variables that, if they had constant setpoints, would result in large losses or infeasibility when there were disturbances (with the remaining degrees of freedom reoptimized). The following controlled variables are recommended for this process: optimally constrained variables, including reactor level (minimum), reactor pressure (maximum), compressor recycle valve (closed), stripper steam valve (closed), and agitator speed (maximum); and unconstrained variables with good self-optimizing properties, including reactor temperature, composition of C in purge, and recycle flow or compressor work. The feasibility of this choice is confirmed by simulations. A common suggestion is to control the composition of inerts. However, this seems to be a poor choice for this process because disturbances or implementation error can cause infeasibility.

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Games have long been considered as a means to support effective learning, motivate learners and accelerate their learning. Several successful studies using game-based learning are reported in the literature. However, there appears to be a research gap on systematically evaluating accelerated learning in game environments. The main research question we address in this paper is how can we evaluate accelerated learning in game-based learning environments? The main contribution of this paper will be a framework for evaluating accelerated learning in games (ALF). We will illustrate the use of this framework by describing studies conducted in the Norwegian industrial project ALTT (Accelerate Learning Through Technology), aimed at capacity building in the aluminium industry, where we have co-designed a game for accelerating learning about the electrolysis process for extracting aluminium and heat balance in the aluminium production cells.

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