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International audience; The use of lignocellulosic biomass in the chemical industry has increased due to its non-competition with the food sector. Several platform molecules can be produced from this biomass. Among them, levulinic acid and its esters have been produced on an industrial scale. There are some reviews on the production of levulinic acid (LA) but few on the production of levulinates (LEs). To fill this gap, this review was written by also considering the environmental aspects. In the first stage, recent progress in the production of these platform chemicals was discussed. Production processes of alkyl levulinates from levulinic acid esterification, precursors (HMF, furfural, etc.), sugar monomers (glucose, fructose, etc.), cellulose, hemicellulose, or cellulose directly from lignocellulosic biomass were described. In the second stage, process separation and purification of LA and LEs were discussed. The final stage proposed an economic and environmental consideration for the production of these chemicals.

The thermodynamic study of beech wood gasification was performed in a pilot scaled fluidized bed reactor at different operation conditions, including temperature range from 600 °C to 900 °C, using CO2 and steam as gasification agents and sand and biochar as bed materials. The particularity of this study was the evaluation of energy consumption of biomass gasifier based on energy balance comparison in the same experimental set-up for different experimental conditions. The comparison between pyrolysis and gasification with CO2 showed that pyrolysis required less heat input (4.4 and 5.0 MJ/kgbiomass) than gasification (6.7 and 7.8 MJ/kgbiomass) at 800 and 900 °C respectively. Meanwhile the syngas CGE were higher for gasification (0.78) as the LHV values for syngas (12.2 MJ/kg). Temperature favoured CGE and syngas LHV increase, as it also increased process endothermicity. The presence of biochar as bed material increased the syngas LHV from 3.3 to 15.4 MJ/kg for CO2 gasification and from 3.3 to 15.1 MJ/kg for steam gasification. Both gasification agents provided similar values of CGE (>0.95) at 900 °C. From 600 to 900 °C the required heat input for steam gasification it was from 1.5 to 6.9 MJ/kgbiomass, meanwhile for CO2 gasification was from 4.1 to 7.1 MJ/kgbiomass. The results offer useful details that can help for future design of gasification experiments in fluidized bed reactors.

The influence of inlet parameters on the production and thermal risk of complex chemical systems can be cumbersome to evaluate. To determine the optimum safe operating conditions, one needs to solve complex differential equations derived from energy and material balances. This robust approach cannot be made on-site, and it is essential to propose simplest tools to evaluate rapidly the performance and safety of some operating conditions. This is the aim of this paper that establishes explicit relationships between the production and thermal risk parameters, and the inlet parameters. In addition, it also proposes a Pareto chart that can be used to make the tradeoff between safety and performance. Such relationships and chart were developed for the production of epoxidized cottonseed oil under isoperibolic and semi-batch mode. The kinetic model developed by Zheng et (Zheng et al., 2016). was used. First, a numerical approach, i.e., least square method, was used to find explicit relationships between thermal risk parameters, production parameters and six inlet parameters. The use of such an approach allows a better understanding of this process. Second, safety and performance indicators are proposed and discussed to evaluate the operating conditions thanks to a simple and intuitive schema. Besides, this approach can be used to find the optimum conditions more rapidly.

This work deals with a new hybrid approach for the detection and diagnosis of faults in different parts of fed-batch and batch reactors. In this paper, the fault detection method is based on the using of Extended Kalman Filter (EKF) and statistical test. The EKF is used to estimate on-line in added to the state of reactor the overall heat transfer coefficient (U). The diagnosis method is based on a probabilistic neural network classifier. The Inputs of the probabilistic classifier are the input–output measurements of reactor and the parameter U estimated by EKF, while the outputs of the classifier are fault types in reactor. This new approach is illustrated for simulated as well as experimental data sets using two cases of reactions: the first is the oxidation of sodium thiosulfate by hydrogen peroxide and the second is alkaline hydrolyse of ethyl benzoate in homogeneous hydro-alcoholic. Finally, the combination of the estimated parameter U using EKF and probabilistic neural network classifier provided the best results. These results show the performance of the proposed approach to monitoring the semi-batch and batch reactors.

International audience; ln the context of a more environmentally friendly and economical process development, the use of microwaves has many advantages such as reduced energy consumption and reduced reaction time. The work presented here focuses on the dehydration of sorbitol to isosorbide in the presence of an acid catalyst in a solvent-free system and under microwave heating. The obtained yield is up to 70%. Different optimization parameters of the reaction are investigated as the temperature, the presence of an inert gas, the influence of the pressure, the agitation speed and the catalyst loading. Several species such as intermediate anhydro-hexitol are identified and quantified and a simplified reaction scheme is proposed. A kinetic study is carried out between 120 °C and 160 °C using different catalyst loadings. Several models considering first order reactions or Langmuir–Hinshelwood type equations are studied. The kinetic parameters obtained are evaluated and discussed. We conclude that the kinetics of the reaction is only correctly described by a Langmuir–Hinshelwood type model, including the adsorption–desorption equilibrium of sorbitol on the catalyst.