The main aim of the proposed research project is to investigate and design new data-driven advanced methods of automatic control and monitoring in process industries to improve efficiency of process plants, their monitoring, and process control and to improve profitability, stability, and competitiveness. We will focus on processes with heat and mass transfer where efficiency can be improved significantly. These processes are inherently complex, exhibit nonlinear and hybrid behaviour that has consequences in control quality and performance. Optimal control and monitoring will cover interplay of techniques of applied statistics, treatment of big data, data-based state estimation, inferential sensors, dynamic optimisation, predictive control. Also, important will be software implementation of proposed solutions, available to a larger community in open-source code as well as verification of the proposed methods in laboratory conditions and with data from industrial partners.