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Enzymatic reactions can successfully replace complex chemical syntheses using milder reaction conditions and generating less waste. The developed model-based numerical analysis turned out to be a beneficial tool to determine the optimal operating policies of complex multienzymatic reactors. As proved, for such cases, the determination of a Fed-Batch Reactor (FBR) optimal operating policy results in a difficult multiobjective optimization problem. Exemplification is made for the bienzymatic reduction of D-fructose to mannitol by using MDH (mannitol dehydrogenase) and nicotinamide adenine dinucleotide (NADH) cofactor with the in situ continuous regeneration of NADH at the expense of formate degradation in the presence of FDH (formate dehydrogenase). For such a coupled system, the model-based engineering evaluations must account for multiple competing (opposable) optimization objectives. Among the multiple novelty elements: i) an optimally operated FBR with a tightly controlled variable feeding (of the time stepwise type) during the batch can lead to higher performance; ii) the optimally operated FBR reported better performance compared to an optimally single or cyclic BR, or to optimally serial batch-to-batch reactors (SeqBR), when considering a multiobjective optimization; iii) the concomitant variable feeding with substrate, enzymes, and cofactor during the FBR “time-arcs” is an option seldom approached in the literature but which is proved here, leading to consistent economic benefits.

Multi-enzymatic reactions can successfully replace complex chemical syntheses, using milder reaction conditions, and generating less waste. The present model-based analysis compares the performances of several optimally operated Batch Reactors (BR) with those of an optimally operated serial Sequence of BRs (SeqBR). In multi-enzymatic systems, SeqBR could be more advantageous and flexible, allowing the optimization of costly enzymes amounts used in each BR in the series. Exemplification was made for the bi-enzymatic reduction of D-fructose to mannitol by using MDH (mannitol dehydrogenase) and the NADH cofactor, with the in situ continuous regeneration of NADH at the expense of formate degradation in the presence of FDH (formate dehydrogenase). For such coupled enzymatic systems, the model-based engineering evaluations are difficult tasks, because they must account for the common species’ initial levels, their interaction, and their dynamics. The determination of optimal operating modes of sole BR or of a SeqBR turns into a multi-objective optimization problem with multiple constraints to be solved for every particular system. The study presents multiple elements of novelty: (i) the proof of higher performances of an optimal SeqBR (including N-BRs) compared to a sole optimal BR operated for N-number of runs and (ii) the effect of using a multi-objective optimization criteria on SeqBR adjustable dynamics.

Model-based evaluation of major accident consequences and effects occurring during the transport of dangerous substances presents a great interest, because it allows derivation of relevant conclusions on the cause-effect close relationship. Such a numerical (in-silico) analysis helps to improve safety regulations for the transport of hazardous substances aimed at preventing dramatic accidents causing many deaths, injuries, and structural damage. By using the standard TNT equivalency math model, coupled with the Probit functions technique, the consequences and effects of an accidental blast have been estimated.1,2 The approached case study here refers to the accidental explosion of a truck while transporting 20 t of ammonium nitrate (AN) in the proximity of Mihăileşti village (Romania) on 24 May 2004. The model-based simulated accident consequences and effects match the data taken on the spot after the accident. Multiple simulations lead to deriving relevant conclusions of practiced value for improving the transport safety of hazardous substances. This work is licensed under a Creative Commons Attribution 4.0 International License.

Bioreactor optimization is a common engineering problem difficult to be solved due to the large number of influential variables of high variability. Production of monoclonal antibody is a well-known method to synthesize a large number of identical antibodies (that is of uniform characteristics, also called monoclonal antibodies, mAb). Due to such reasons intense efforts have been invested to maximize the production of mAb by using hybridoma cell culture. Based on an adequate kinetic model from literature (experimentally checked) this paper focus on pointing-out the major role of the net evolution of the viable biomass (growth, and decay) in the location of the optimal operating setpoint (SP) of a three-phase mechanically agitated batch bioreactor (TPMAB) with immobilized hybridoma culture. This in-silico analysis opens the possibility I) to optimize the bioreactor performances by placing the SP in the most favourable location, by adjusting the substrate and biomass initial load in the bioreactor according to the preliminary determined characteristics of a modified / improved biomass; ii) to optimize the batch-to-batch operation mode (not approached here) according to the time-varying characteristics of the biomass culture, or iii) to determine the optimal operation of the bioreactor in a fed-batch operating mode (not approached here).

Autonomous oscillations of glycolytic intermediates’ concentrations reflect the dynamics of the control and regulation of this major catabolic pathway in living cells. In spite of its major role in the cell central carbon metabolism (CCM), glycolysis interference with other metabolic pathways has seldom been investigated. However, the glycolysis model module is the ‘core’ part of any systematic and structured model-based analysis of the cell metabolism. On the other hand, tryptophan (TRP) synthesis is another oscillatory metabolic process of high practical interest in the biosynthesis industry, and in medicine. Because the TRP synthesis is strongly connected to the glycolysis through the PEP (phosphoenolpyruvate) node, based on two reduced kinetic models for glycolysis and TRP synthesis, this paper performs, for the first time, an in silico analysis of the way by which the two oscillatory processes interfere in the E. coli cells. The analysis allows guidance of tryptophan synthesis optimization.

This paper presents a novel methodology to develop complex structured bi-level (hybrid) kinetic models of some important cell bioprocesses. The approached case study refers to the tryptophan (TRP) synthesis in E. coli cells. The required experimental data were collected from a fed-batch bioreactor (FBR). The developed structured kinetic model includes several inter-connected reaction pathway ( modules ) able to simulate the kinetics of glycolysis, TRP-operon expression, ATP-recovery system, all belonging to the central carbon metabolism (CCM). Experiments were carried out by using an E. coli strain modified to replace the PTS-system with a more efficient one to uptake the glucose (GLC) from the environment. By linking the FBR macroscopic state variables with the nano-scale variables describing the cell metabolic processes of interest, the resulted structured hybrid dynamic model presents a large number of advantages, such as: allows further in-silico (model-based) more accurate engineering developments; it can be used for bioinformatics purposes.

One essential engineering problem when developing an industrial enzymatic process concerns the choice of the reactor operating alternative based on a-priori knowledge of the process kinetics and enzyme inactivation characteristics. For a multi-enzymatic system, involving complex interactions among enzymes that exhibit optimal activity on different parametric domains, and a high-order deactivation, this problem requires an extended analysis. The optimization and control engineering problems become very challenging due to the process high nonlinearity and the presence of a large number of complex constraints. All these will translate into a multi-objective optimization problem to be solved for each case. An elegant option developed in this paper is to obtain sets of Pareto optimal solutions, also called Pareto-optimal fronts, successively generated for pairs of adverse objectives. Then, the final choice of the enzymatic reactor operating policy results from the comparative analysis of these Pareto-fronts. Exemplification is made for the complex case of the oxidation of D-glucose (DG) to 2-keto- d -glucose (kDG) in the presence of P2Ox (Pyranose oxidase, EC 1.1.3.10) and catalase, continuously operated in a three-phase mechanically agitated semibatch reactor (MASCR) with co-immobilized enzymes on alginate beads. Optimal operating policy choice is based on the minimum amount of required P2Ox that ensures an imposed reaction conversion and maximum reactor productivity under various technological constraints, and for catalase/P2Ox ratios of 200–300 U/U.

One essential engineering problem when developing an industrial enzymatic process concerns the model-based design and optimal operation of the enzymatic reactor based on the process and enzyme inactivation kinetics. For a complex enzymatic system, the “default” used first-order enzyme deactivation model has been proved to lead to inadequate process design or sub-optimal operating policies. The present study investigates if a complex enzyme deactivation can be approximated with simple 1st, 2nd, or a novel proposed model with variable deactivation constant. The approached complex enzymatic system is those of the oxidation of D-glucose to 2-keto-D-glucose in the presence of pyranose 2-oxidase. The necessary “simulated experimental data” have been generated by means of an extended kinetic model from literature used to simulate a batch reactor under well-defined nominal conditions. The proposed enzyme deactivation model has been found to be the best lumping alternative, presenting several advantages: simplicity, flexibility, and a very good adequacy.

Autonomous oscillations of glycolytic intermediate concentrations reflect the dynamics of the control and regulation of this major catabolic pathway, and this phenomenon has been reported in a broad range of bacteria. Understanding glycolytic oscillations might therefore prove crucial for the general understanding of the regulation of cell metabolism with immediate practical applications, allowing in silico design of modified cells with desirable ‘motifs’ of practical applications in the biosynthesis industry, environmental engineering, and medicine. By using a kinetic model from literature, this paper is aiming at in silico (model-based) identification of some conditions leading to the occurrence of stable glycolytic oscillations in the E. coli cells. This work is licensed under a Creative Commons Attribution 4.0 International License.

Modelling bacteria glycolysis is a classical subject but still of high interest. Glycolysis, together with the phosphotransferase (PTS)-system for glucose transport into the cell, the pentose-phosphate pathway (PPP), and tricarboxylic acid cycle (TCA) characterize the central carbon metabolism. Such a model usually serves as the foundation for developing modular simulation platforms used for consistent analysis of the control / regulation of target metabolite synthesis. The present study is focused on analyzing the advantage and limitations of using a simplified but versatile ‘core’ model of mTRM) of glycolysis when incomplete experimental information is available. Exemplification is made for a reduced glycolysis model from literature for Escherichia coli cells, by performing a few modifications (17 identifiable parameters) to increase its agreement with simulated data generated by using an extended model (127 parameters) over a large operating domain of an experimental bioreactor. With the expense of ca. 8–13 % increase in the relative model error vs. extended simulation models, derivation of reduced kinetic structures to describe some parts of the core metabolism is worth the associated identification effort, due to the considerable reduction in model parameterization (e.g. 17 parameters in mTRM vs. 127 in the extendedChassM model of Chassagnole et al.), while preserving a fair adequacy over a wide experimental domain generated in-silico by using the valuable extended ChassM. The reduced model flexibility is tested by reproducing stationary or oscillatory glycolysis conditions. The reduced mTRM model includes enough information to reproduce not only the cell energy-related potential through the total A(MDT)P level, but also the role played by ATP/ADP ratio as a glycolysis driving force. The model can also reproduce the oscillatory behaviour occurrence conditions, as well as situations when homeostatic conditions are not fulfilled.

a b s t r a c t A bi-level model is proposed by coupling a three-phase fluidized bioreactor (TPFB), used for mercury uptake from wastewaters by immobilized Escherichia coli cells, with a cellular simulator of the genetic regulatory circuit (GRC) controlling the mercuric ion reduction in the cytosol. While keeping a reasonable agreement with the experimental data from literature (free cell cultures with/without cell membrane permeabilization), the structured model advantages are coming from the prediction detailing degree [simulated 26 + 3 (cell + bulk) vs. 3 (bulk) variable dynamics] covering a wide range of input Hg2+ loads (0–100 mg L −1 ), and cloned E. coli cells with various amounts of mer-plasmids (3–140 nM). The model offers the possibility to predict the inner cell mercury reduction rate (different from the apparent rate observed in the bioreactor), the bacteria metabolism adaptation to environmental changes over several cell cycles, and the effect of cloning cells to modify their behaviour under stationary or perturbed conditions.

Safe operation of a catalytic reactor remains a sensitive issue when highly exothermic reactions are conducted and hazardous side reactions may occur. Derivation of the optimal operating conditions must include economic but also safety criteria, technological constraints, beside controllability and stability aspects. The present work introduces a criterion based on a joint failure probability index related to uncertainty in the runaway boundaries and the random disturbances of the operating parameters. The use of such a safety criterion is even more important when setting the optimal operating policy of the reactor in the vicinity of the runaway boundaries that often correspond to a high productivity. The paper indicates how an economically efficient but more prudent operating policy can be selected, by simultaneously considering the economic and safety objectives. An example is provided for the case of an industrial fixed-bed tubular reactor, of high thermal sensitivity, used for the catalytic oxidation of butane to maleic anhydride in vapour phase. The multi-objective optimization can lead to a prudent trade-off operating solution (corresponding to a failure probability of maximum 3–4%) that limits the reactor productivity. Being based on a local and global sensitivity analysis of the reactor, the proposed rule is generally applicable by minimizing the probability with which control variables violate their safety limits.

Design and optimal operation of an enzymatic process depend on the characteristics of the free- vs. immobilized-enzyme, purification problems, materials and operation costs. When the enzyme stabilization is not fully successful, the optimal choice of the reactor type and operation mode is depending on the process and enzyme deactivation characteristics. Quick simulation of operation modes based on the process kinetics and a library of reactor models can help in comparing alternatives for getting the final decision. The analysis is exemplified for two enzymatic processes, by determining the optimal operating policy for several tested reactors: classic batch, batch with intermittent addition of enzyme, semi-batch reactor, continuously operated packed-bed column, or a mechanically agitated reactor with immobilized enzyme in small beads. Prediction of reactor performances points out the best choice for the given enzyme characteristics, and also conditions allowing to switch the production from large-volume batches to the continuous operation mode.

A large number of optimization strategies have been developed for semi-batch reactors (SBR) with a fixed or free terminal time, with or without considering various sources of uncertainty. Such strategies account for safety constraints rather empirically determined in the form of parameter thresholds, while safety indices are seldom integrated in the optimization objective function. The present work illustrates how the runaway boundaries, and their confidence region associated to the parameter uncertainty, can be evaluated using the process model and a generalized sensitivity criterion, and how they can be included in the SBR optimization. A concrete example is provided for the SBR used for the acetoacetylation of pyrrole with diketene in homogeneous catalysis, a process known to be of high risk due to the very exothermic side-reactions. While previous studies approached the isothermal SBR, the present work is focused on optimizing the non-isothermal SBR operation.

The safety in operation of a fixed-bed catalytic reactor remains a sensitive issue when a highly exothermic reaction is conducted and various process development elements such as controllability, stability, risk, and economic aspects are considered. Several model-based methods are used to estimate the safe operating region limits. Nominal conditions are set to limit the hot spot in the tubular reactor and avoid excessive thermal sensitivity to variations in the process parameters. When the catalyst or its characteristics are changed, the operating conditions have to be adjusted accordingly. The safety problem becomes more important when the production optimization requires setting the nominal operating point in the vicinity of the safety limits. This paper investigates the advantages as well as the precautions that need to be taken when using a more sophisticated model-based global sensitivity criterion (MV of Morbidelli & Varma) to routinely update the runaway critical conditions when changes in the investigated system frequently occur. A concrete example is provided for the case of an industrial fixed-bed catalytic reactor for nitrobenzene hydrogenation in vapour-phase. The analysis points out the discrepancies in predicting the runaway boundaries for complex processes between precise sensitivity-based MV-method and shortcut methods, and the importance of accounting for parameter uncertainty for both evaluation of the confidence region around the runaway boundaries and for the optimal set-point location. The close connection between the operating risk limits and the process kinetics is also highlighted even if the reactor geometry and the main flow conditions are kept unchanged.

When highly exothermic/hazardous reactions are conducted in the presence of parametric uncertainty, derivation of optimal operating policies for a chemical reactor has to simultaneously consider several objectives of sustainability. The paper uses an original methodology to generate the Pareto optimal solutions when reactor productivity and safety objectives (expressed in probabilistic terms) are simultaneously considered using the process and reactor model in a simple way, in the presence of technological constraints, uncertainty in safety boundaries, and random fluctuations in control variables. An example is provided for the industrial fixed-bed tubular reactor used for the catalytic hydrogenation of nitrobenzene to aniline in vapour-phase.

Model-based optimization of batch enzymatic/bio-reactors is an intensively studied problem due to the increased economic benefits offered by the predicted optimal operating policies. The enzymatic oxidation of d -glucose (DG) in a batch reactor is a process of industrial interest, leading to production of “rare sugars” (keto-derivatives) from monosaccharides. The enzyme activity is sensitive to operating conditions, deactivation occurring due to some of the products and impurities. To keep a high catalytic activity during the batch, a pulsate addition of enzyme solution is performed under some restrictions. By imposing the maximum batch operation time and injected volume, operating policies maximizing the DG-conversion can be derived for every batch case. In the present study, by accounting the process kinetic characteristics, a pulse-injection control function, of exponential type, is proved to be easily adaptable for the optimization of the batch reactor. The heuristic near optimal solution is simple, flexible and can efficiently replace the optimal impulse-fed-policy derived by means of the time-consuming classical direct methods. The heuristic rule, based on the process kinetic model and on a library of semi-empirical functions, can be easily applied to optimize various batch enzymatic processes.

To better understand and “program” the drug release in human plasma, a kinetic characterization of the process is necessary for every drug ligand and macromolecular support. Due to few observed species and limited standard information, process modelling based on extended kinetic structures is difficult. Laboratory experiments that mimic the natural conditions and the bio-environment, together with statistical estimators allow identification of reduced (apparent) kinetic models. The scope of this paper is to present a more systematic approach to interpret the kinetic models and to link the identified apparent rate/equilibrium constants to kinetic characteristics of an extended “intrinsic” reaction path. The developed stoichiometric analysis and linear lumping rules are exemplified for a case study from literature, that considers a four-ligands (“drug” dansyl groups) release from a dendrimeric support structure in a reducing environment that mimic the human plasma.

The enzymatic oxidation of d -glucose to 2-keto- d -glucose ( d -arabino-hexos-2-ulose, d -glucosone) is of prospective industrial interest. Pyranose oxidase (POx) from Peniphora gigantea is deactivated during the reaction. To develop a kinetic model including the main reaction and the enzyme inactivation, possible side-reactions of the non-stabilised enzyme with d -glucosone, hydrogen peroxide, and peroxide radicals were considered. A developed step-by-step combined experimental and computational procedure allowed to discriminate among alternative inactivation mechanisms and provides an increased model reliability. The most probable scheme is the enzyme inactivation by hydroxyl radicals formed from produced H 2 O 2 in the presence of Fe 2+ ions. This OH reaction is supported by matrix assisted laser desorption ionisation–mass spectrometry (MALDI–MS) measurement. The estimated kinetic parameter values for the main reaction are of the same order of magnitude as those reported in the literature. The identified model allows a satisfactory process simulation and highlights measures to prevent the enzyme activity loss.

The biological treatment is the most complex step in removing organic and inorganic pollutants from wastewaters, being very sensitive to input-flow oscillations, operating conditions, and biomass evolution. Sudden increases in substrate concentration or some inhibitory substances, deterioration of the biomass, or few observed species, all of these lead to a difficult process modelling and need replaced biokinetics identification for each waste and biomass type. However, the bioprocess numerical analysis is crucial for obtaining significant improvements in the wastewater treatment (WWT) plant performances and safety indices even under imperfect data. The paper exemplifies an advanced route to quickly on-line identify the biodegradation characteristics of new substrates processed by a series of perfectly mixed aeration basins with biomass recycle. The Monod kinetics is recursively identified by using the available collection of plant previous transient operating data and a robust shortcut estimator (MIP). The approximate solution is periodically refined with an exact nonlinear estimator (NLS), checked for consistency and significance versus prior information, and stored in databanks.

One of the hazards of most concern in the chemical industry is the thermal hazard , which is governed by the thermodynamics and kinetics of the usually complex chemical reactions. To develop risk analysis indices the approximate knowledge of the reaction path, kinetics and thermal effects are crucial elements. While standardised tests to experimentally determine the ‘thermal stability data’ and assessment procedures for runaway reactions are developed, their interpretation is difficult to be generalised because they have been developed in the context of a specific process, and they often contain empirical as well as rationally deduced elements and rules. This paper illustrates major advantages and result improvements in thermal risk analysis when two short-cut techniques are combined: the experimental ‘Differential Scanning Calorimetry’ ( DSC ) and the numerical ‘Modified Integral Transformation Procedure’ ( MIP ). The DSC is useful in investigating the n -th order kinetics and overall thermal effect of a reaction following an elaborated strategy; attempts to fit more complex kinetics fail especially in the model discrimination step. The MIP ( Maria and Rippin, 1997 ) performs a short-cut kinetic model identification by considering previous information stored in databanks and incomplete information about the process. By coupling the DSC and MIP techniques quick identification of a (reasonable) complex chemical kinetics becomes possible. Exemplification is made for an auto-catalytic process with incomplete data by obtaining: i) a more exact evaluation of the safety index TMR ad compared with those derived from classical DSC ; ii) a more robust estimate of the kinetics by using the databank information in overcoming poor-conditioning due to incomplete data.

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 & Flammersheim, 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.

In both small and large-scale investigations, a reliable short-cut procedure to estimate the approximate parameters is very useful for the successive rapid checking of different Kinetic Model ( KM ) structures for their adaptation to current process data. An improved quality of the initial parameter guess also improves the reliability and the convergence rate for a subsequent exact Nonlinear Least Squares ( NLS ) regression technique applied for fitting the final model. The recently proposed Modified Integral transformation Procedure ( MIP ) short-cut estimation method of Maria and Rippin (1995) [ Computers and Chemical Engineering 19 (Supplement), S709–S714 (1995)] adds supplementary elements of similarity analysis and prior information about similar model structures to the classical Integral transformation Procedure ( IP ) for kinetic parameter estimation. By exploiting the model structure and the interactive use of information stored in a kinetic databank, the MIP makes rapid adaptation of a KM and parameters, describing an already studied process, to a similar process under study with only the product distribution known. The problem decomposition and the term-by-term sensitivity and estimation analysis of the model for various portions of experimental data sets result in a very effective MIP . The generated initial parameter estimate is more reliable and of better quality compared with the classical direct techniques, especially for non-linear and ill-conditioned cases. Algebraic transfer of information functions are developed in interaction with the kinetic databank, leading to a rapid check of different kinetics, or the same kinetic model for different data sets, without time-consuming intermediate NLS steps. The MIP was integrated in an expert system for kinetic identification and coupled with statistical data/estimate analysis (Maria, 1993 [ Computers and Chemical Engineering 17 (Supplement), S435–S440 (1993)]; Maria and Rippin, 1996 [ Computers and Chemical Engineering 20 (Supplement), S587–S592 (1996)]). MIP implies any iterative search, it has no convergence problems and requires no tuning factor. The basic MIP , developed for isothermal data treatment, is also shown to be suitable for on-line kinetics identification in (semi-) batch processes. The interaction with the prior information allows on-line adaptations of the model structure and parameters, comparable with extended Kalman Filter ( EKF )-based recursive estimators. In the present work these results are also extrapolated for linear kinetics estimation by using non-isothermal data.

The methylcyclohexane ( mch ) dehydrogenation step to recycle toluene and release hydrogen ( hyd ) for various applications is being studied as part of a hydrogen energy storage project. The reaction is performed catalytically in a fixed bed reactor, and the efficiency of this step significantly determined overall system economics. The fresh catalyst kinetics and the deactivation of the catalyst play an important role in the process analysis. To predict the catalyst performance at various coke content levels, laboratory experiments at different scales were carried out in three stages: 1) isothermal experiments at low mch conversions in a differential microreactor; 2) non-isothermal experiments in PC-controlled integral reactors; 3) increasing temperature experiments in two integral rectors in series to deposit significant coke levels. A wide range of operating conditions were used: 200–400 hours continuous operation, high liquid hourly space velocities ( LHSV ) of 3–20 1/hr., 0–10 hyd/mch feed ratio, 6–16 atm overall pressure, 250°C –550°C temperature in the reactors. Axial species concentrations and temperature profile measurements at different pressure levels and time-on-stream allowed a study of the catalyst deactivation effects for mch conversions up to 99%. The main reaction kinetics were selected by using a parameter sensitivity analysis for model discrimination and the kinetics short-cut identification software package of Maria (1993), Maria and Rippin (1995). The resulting two-parameter model was further tested with experiments in the dual integral reactor system. The activation energy for the main reaction was estimated at 220 ± 11 kJ/mol. A similar sensitivity analysis was used to derive a reduced model for catalyst deactivation, by using data recorded in the dual non-isothermal integral reactor system. The best results were obtained with a zero-order deactivation kinetics. A simple dependence between the catalyst activity and the coke content on the catalyst (up to 17 wt% for significant time-on-stream) was tested.

In the simulation of complex reactions, the use of mechanistic based kinetic models (KM), although requiring extensive experimental and computational effort, presents the advantage of increased prediction reliability and physically meaningful estimated parameters. Because rapid off/on-line process simulation and optimisation usually require reduced KM-s, repeated parameter and model structure adaptations are necessary. Recently, Maria and Rippin (1995a,b) and Maria (1995) proposed a reliable short-cut technique (MIP, the Modified Integral transformation Procedure) for rapid model identification and approximate parameter estimation. The MIP only implies rapid algebraic manipulations and does not present any convergence problems. Supplementary elements of reaction path recognition (similarity analysis, problem decomposition, alternative path discrimination, transfer of information rules), and model term-by-term sensitivity and estimate analysis, make the MIP solution more robust and of considerable improved quality compared with the classical direct estimation procedures. The procedure is very suitable for non-linear and ill-conditioned cases, being less sensitive to the noise level, outlier presence, or data and model degeneracy. The MIP, integrated in an expert system for kinetic modelling, allows a rapid kinetic data-bank check for suitable KM selection and adaptation to the new considered data. The MIP could also be used as a recursive parameter estimator by transferring previous information about the current process, without use of tuning factors or model linearizations during the identification rule. In this paper some completions to the MIP method are presented in order to improve the initial step when little prior information about the process is available: i) fast identification of a similar KM structure in the data-bank and discrimination among extended or reduced reaction path schema; ii) initial use of other direct estimation techniques and few data from the process to generate rough prior KM estimates; iii) initial use of non-linear regression (NLS) steps and few data from the process to initiate the MIP recursive estimation.