A computer-implemented method of configuring a control device for closed-loop control in a plant for production of food products, said method comprising the steps of: obtaining definition data for a sub-system, which is included in the plant and is operated by the control device, wherein the definition data is indicative of - a task to be performed by of the sub-system in the production of the food products, and - a combination of components (11A, 11B, 11) included in the sub-system to perform the task; deriving, based on the definition data, a candidate process model of the sub-system comprising one or more differential equations that represent the task to be performed by the sub-system by use of the combination of components (11A, 11B, 11) in the production of the food products; obtaining measurement data that is generated by the sub-system when operated in accordance with a test sequence; estimating a set of constant parameters of the one or more differential equations based on the test sequence and the measurement data; defining a final process model for the sub-system based on the one or more differential equations and the set of constant parameters; and operating a tuning algorithm on the final process model to determine one or more control parameters of the control device for the closed-loop control, characterized in that said step of estimating the set of constant parameters comprises operating a fitting algorithm on the candidate process model, given the test sequence and the measurement data, to determine a parameter vector that contains estimated values of the set of constant parameters, wherein the fitting algorithm is configured to modify the parameter vector until the candidate process model, when configured by the parameter vector, is deemed to produce the measurement data from the test sequence, and subsequently output the parameter vector, and said step of deriving comprises deriving a plurality of candidate process models of the sub-system based on the definition data, and wherein said step of estimating comprises operating the fitting algorithm on each of the candidate process models, given the test sequence and the measurement data, to determine a respective parameter vector, and wherein said defining comprises: selecting one of the candidate process models, and defining the final process model based on the one or more differential equations of the thus-selected candidate process model and its parameter vector. obtaining definition data for a sub-system, which is included in the plant and is operated by the control device, wherein the definition data is indicative of - a task to be performed by of the sub-system in the production of the food products, and - a combination of components (11A, 11B, 11) included in the sub-system to perform the task; - a task to be performed by of the sub-system in the production of the food products, and - a combination of components (11A, 11B, 11) included in the sub-system to perform the task; deriving, based on the definition data, a candidate process model of the sub-system comprising one or more differential equations that represent the task to be performed by the sub-system by use of the combination of components (11A, 11B, 11) in the production of the food products; obtaining measurement data that is generated by the sub-system when operated in accordance with a test sequence; estimating a set of constant parameters of the one or more differential equations based on the test sequence and the measurement data; defining a final process model for the sub-system based on the one or more differential equations and the set of constant parameters; and operating a tuning algorithm on the final process model to determine one or more control parameters of the control device for the closed-loop control, characterized in that said step of estimating the set of constant parameters comprises operating a fitting algorithm on the candidate process model, given the test sequence and the measurement data, to determine a parameter vector that contains estimated values of the set of constant parameters, wherein the fitting algorithm is configured to modify the parameter vector until the candidate process model, when configured by the parameter vector, is deemed to produce the measurement data from the test sequence, and subsequently output the parameter vector, and said step of deriving comprises deriving a plurality of candidate process models of the sub-system based on the definition data, and wherein said step of estimating comprises operating the fitting algorithm on each of the candidate process models, given the test sequence and the measurement data, to determine a respective parameter vector, and wherein said defining comprises: selecting one of the candidate process models, and defining the final process model based on the one or more differential equations of the thus-selected candidate process model and its parameter vector.