The chemical industry uses complicated reacting systems under a wide range of physical conditions. Besides production maximisation, stable and safe operation are important goals. This objective has to be considered in all the engineering calculations from the process design phase until the plant operation. Modelling and simulation of the processes accelerates and improves design. It may include qualitative information and/or heuristic rules. Process modelling, optimisation and control allow safe plant design and operation if enough information about the process is available. An experimental and a numerical technique will be combined in this paper. The ＇Differential Scanning Calorimetry＇ (DSC; Hohne, Hemminger & FIammersheim, 1996, Differential scanning calorimetry) is an experimental technique that can be used to investigate the mechanism and kinetics of a chemical process by measuring the thermal effect of the reaction following a very elaborated strategy. At early stages in process design, DSC is used as a screening tool to assess the thermal safety. Under the assumption of zero order Arrhenius kinetics, the activation energy and therefore the time to maximum rate under adiabatic conditions (TMRad) may be estimated (ANSI/ASTM, E 698-79; Keller, Stark, Fierz, Heinzle & Hungerbuhler, 1997, J. Loss Prev. Process Ind., 10, 31-41). A similar way can be applied to determine the kinetic constants and TMRad for an nth order reaction. Attempts to fit more complex kinetic models to DSC thermograms encounter many difficulties; one of them is model discrimination, a question not yet answered. The ＇Modified Integral Transformation Procedure＇ (MIP; Maria & Rippin, 1997, Comp. & Chem. Eng., 21, 1169-1190) was proposed for quick process identification by considering previous information stored in data-banks and incomplete information about the process. The estimation technique is effective even if few but distributed process data are available and it can be easily coupled with other statistical data analysis and estimation techniques. The MIP is integrated in an expert system for process identification (Maria & Rippin, 1996, Comp. & Chem. Eng., S20, S587-S592) which facilitates computer-based plant analysis. It is the scope of this paper to investigate the effectiveness of using these coupled experimental and numerical short-cut techniques and an interactive data-bank in quick identification of complex chemical kinetics. If model discrimination is not possible, an experimental procedure to close existing data gaps most efficiently can be developed. The identified model can be further used in predicting optimum operating conditions for a chemical process by considering the desired product maximisation, waste and by-product minimisation, safety and operability as goals. The developed quick experimental and PC-coupled numerical identification and process analysis are exemplified in some simple complex kinetic cases. The effectiveness of the elaborated calculation methodology is discussed together with possibilities of further improvements.