A method of controlling a chemical process carried out in a large-scale chemical plant, the chemical plant comprising subsystems characterised in that they are independently operable assemblies whose function has a direct influence on production or the production flow, comprising the following steps: - Sensory recording of process data from the chemical plant, - Transmission of the process data to a control system via a field bus, - Transmission of at least a subset of the process data from the control system to a computer system, wherein the computer system has a simulation program for a steady-state and/or dynamic process simulation of the chemical process, a control program for implementing a controller for the chemical process and a memory for storing simulated state variables, - cyclically repeated calculation of the simulated state variables of the chemical process by the simulation program from the subset of the process data and storage of the simulated state variables in the memory, - Transmission of at least one setpoint value of a controlled variable of the chemical process to the control program, - Reading at least a subset of the simulated state variables from the memory for input into the control program, - Determination of a manipulated variable to achieve the setpoint value by the control program by processing the simulated state variables read from the memory, - Transmission of the calculated actuating variable to the control system, - wherein the manipulated variable is transmitted from the control system via the field bus to an actuator, wherein - each subsystem comprises one or more actuators, wherein - the actuator adjusts the subsystem in accordance with the calculated manipulated variable, characterised in that - the computer system includes a machine learning module, wherein training data sets are used to train the machine learning module, which training data sets contain at least the subset of the simulated state variables and the manipulated variables calculated therefrom by the control program, wherein - the training data records include the response of the chemical plant to the manipulated variable contained in the process data, wherein - after training the machine learning module, the control program is switched over to the machine learning module so that the latter is replaced by the machine learning module and so that the manipulated variable is determined from the read state variables by the machine learning module. - Sensory recording of process data from the chemical plant, - Transmission of the process data to a control system via a field bus, - Transmission of at least a subset of the process data from the control system to a computer system, wherein the computer system has a simulation program for a steady-state and/or dynamic process simulation of the chemical process, a control program for implementing a controller for the chemical process and a memory for storing simulated state variables, - cyclically repeated calculation of the simulated state variables of the chemical process by the simulation program from the subset of the process data and storage of the simulated state variables in the memory, - Transmission of at least one setpoint value of a controlled variable of the chemical process to the control program, - Reading at least a subset of the simulated state variables from the memory for input into the control program, - Determination of a manipulated variable to achieve the setpoint value by the control program by processing the simulated state variables read from the memory, - Transmission of the calculated actuating variable to the control system, - wherein the manipulated variable is transmitted from the control system via the field bus to an actuator, wherein - each subsystem comprises one or more actuators, wherein - the actuator adjusts the subsystem in accordance with the calculated manipulated variable, characterised in that - the computer system includes a machine learning module, wherein training data sets are used to train the machine learning module, which training data sets contain at least the subset of the simulated state variables and the manipulated variables calculated therefrom by the control program, wherein - the training data records include the response of the chemical plant to the manipulated variable contained in the process data, wherein - after training the machine learning module, the control program is switched over to the machine learning module so that the latter is replaced by the machine learning module and so that the manipulated variable is determined from the read state variables by the machine learning module. - the computer system includes a machine learning module, wherein training data sets are used to train the machine learning module, which training data sets contain at least the subset of the simulated state variables and the manipulated variables calculated therefrom by the control program, wherein - the training data records include the response of the chemical plant to the manipulated variable contained in the process data, wherein - after training the machine learning module, the control program is switched over to the machine learning module so that the latter is replaced by the machine learning module and so that the manipulated variable is determined from the read state variables by the machine learning module.