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The state-space neural network paradigm is a neural model suitable for various applications in the field of control engineering. In this paper, it is shown how this neural model can be applied to three common tasks in control engineering: modelling of a diffusion section in a sugar industry, prediction in a wastewater plant, and neural model-based predictive control in a sugar factory. Results from these applications show the applicability and good performance of this neural model that, together with the theoretical results available for this type of neural model, gives an excellent alternative to classical linear models in cases where the non-linearity of the system requires it.