Traditional industrial process control activities relevant to multi-objective optimization problems,
such as proportional integral derivative (PID) parameter tuning and operational optimizations, always demand for
process knowledge and human operators’ experiences during human-computer interactions. However, the impact of
human operators’ preferences on human-computer interactions has been rarely highlighted ever since. In response
to this problem, a novel multilayer cognitive affective computing model based on human personalities and pleasurearousal-
dominance (PAD) emotional space states is established in this paper. Therein, affective preferences are
employed to update the affective computing model during human-machine interactions. Accordingly, we propose
affective parameters mining strategies based on genetic algorithms (GAs), which are responsible for gradually
grasping human operators’ operational preferences in the process control activities. Two routine process control
tasks, including PID controller tuning for coupling loops and operational optimization for batch beer fermenter
processes, are carried out to illustrate the effectiveness of the contributions, leading to the satisfactory results.
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