Modelling of methanol synthesis in a network of forced unsteady-state ring reactors by artificial neural networks for control purposes

A numerical model based on artificial neural networks (ANN) was developed to simulate the dynamic behaviour of a three reactors network (or ring reactor), with periodic change of the feed position, when low-pressure methanol synthesis is carried out. A multilayer, feedforward, fully connected ANN was designed and the history stack adaptation algorithm was implemented and tested with quite good results both in terms of model identification and learning rates. The influence of the ANN parameters was addressed, leading to simple guidelines for the selection of their values. A detailed model was used to generate the patterns adopted for the learning and testing phases. The simplified model was finalised to develop a model predictive control scheme in order to maximise methanol yield and to fulfil process constraints

Fast model predictive control for hydrogen outflow regulation in ethanol steam reformers

© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In the recent years, the presence of alternative power sources, such as solar panels, wind farms, hydropumps and hydrogen-based devices, has significantly increased. The reasons of this trend are clear: contributing to a reduction of gas emissions and dependency on fossil fuels. Hydrogen-based devices are of particular interest due to their significant efficiency and reliability. Reforming technologies are among the most economic and efficient ways of producing hydrogen. In this paper we consider the regulation of hydrogen outflow in an ethanol steam reformer (ESR). In particular, a fast model predictive control approach based on a finite step response model of the process is proposed. Simulations performed using a more realistic non-linear model show the effectiveness of the proposed approach in driving the ESR to different operating conditions while fulfilling input and output constraints.Peer ReviewedPostprint (author's final draft

DYNAMICS AND CONTROL OF FORCED UNSTEADY-STATE CATALYTIC REACTORS

This research deals with the dynamics and control of forced unsteady-state catalytic reactors and it is focused on two topics: 1. auto-thermal after-treatment of lean VOC mixtures. Two reactor configurations have been taken into consideration: the reverse-flow reactor (RFR), where the flow direction is periodically changed, and the network of two or three reactors (RN), where the flow direction remains the same, but the feeding position is periodically changed, thus simulating a moving bed. This study (§3) has been organised as follows: - modelling of the two reactor configurations and study of the influence of the main operating parameters (§3.1 and §3.2). As the RFR shows higher stability with respect to disturbances in the feed a deeper investigation has been carried out on this device; - optimisation of the RFR. A simplified model has been used for this analysis in order to strongly reduce the computational effort which is required by detailed models. It has been pointed out that both heat capacity and thermal conductivity of the catalyst play a role, not less important than kinetic activity, strongly influencing the minimum inlet VOC concentration required for autothermal operation (§3.3); - experimental validation of the modelling results in a bench-scale RFR with reduced influence of the wall effects. This activity has been carried at the Departamento de Ingeniería Química y Tecnología del Medio Ambiente-Universidad de Oviedo (Spain) in the framework of the Research Project "Azioni Integrate Italia-Spagna", granted by the Italian Ministry of Research (MIUR). In addition to the intrinsecally dynamic behaviour of the RFR, one must deal with unexpected external perturbations (feed concentration, composition and temperature) which may lead to reactor extinction or catalyst overheating. In order to avoid these problems it is necessary to implement some closed-loop control strategy based on the measurement of the inlet concentration (and composition) and the outlet conversion. This study has been organised as follows: - a model-based soft-sensor (observer) has been developed, in order to quickly and reliably estimate the feed composition from some temperature measurements in the reactor, thus avoiding expensive hardware sensors and time consuming on-line measurements. As deriving an observer from a detailed model is an overwhelming task, a simplified model has been developed and validated in a medium size RFR. This research has been carried out in cooperation with prof. H. Hammoury and D. Schweich of the CPE-Lyon, France (§4.1); - a Model Based control strategy has been proposed and tested to prevent reaction extinction and catalyst overheating (§4.2); 2. enhancement of conversion and selectivity in exothermic, equilibriumlimited reactions. Methanol synthesis and syngas prouction by partial oxidation of methane have been considered as test reactions. This section has been organised as follows: - modelling of the two processes in the two reactor configurations previously described. The influence of the main operating conditions has been addressed with the aim to optimise the two processes. As the RN has shown higher conversion and selectivity with respect to the RFR, in the following the research will be focused on this device (§5); - a simple open loop control policy, which can be useful for a safe startup, has been also tested to study the response of the RN to disturbances on the input parameters, showing that a more robust control strategy is needed for this application; - if a tight control on the outlet product conversion is needed, a Model Predictive Control scheme (MPC) should be used, varying the switching time to maximise the conversion and the selectivity of the reactor. The on-line optimisation requires a simplified model and a Neural Network based model has been developed (§6

ECUT (Energy Conversion and Utilization Technologies Program). Biocatalysis Project

Presented are the FY 1985 accomplishments, activities, and planned research efforts of the Biocatalysis Project of the U.S. Department of Energy, Energy Conversion and Utilization Technologies (ECUT) Program. The Project's technical activities were organized as follows: In the Molecular Modeling and Applied Genetics work element, research focused on (1) modeling and simulation studies to establish the physiological basis of high temperature tolerance in a selected enzyme and the catalytic mechanisms of three species of another enzyme, and (2) determining the degree of plasmid amplification and stability of several DNA bacterial strains. In the Bioprocess Engineering work element, research focused on (1) studies of plasmid propagation and the generation of models, (2) developing methods for preparing immobilized biocatalyst beads, and (3) developing an enzyme encapsulation method. In the Process Design and Analysis work element, research focused on (1) further refinement of a test case simulation of the economics and energy efficiency of alternative biocatalyzed production processes, (2) developing a candidate bioprocess to determine the potential for reduced energy consumption and facility/operating costs, and (3) a techno-economic assessment of potential advancements in microbial ammonia production

Coal mine ventilation air methane combustion in a catalytic reverse flow reactor: Influence of emission humidity

The role of the humidity content on the performance of catalytic reverse flow reactors (RFRs) for the abatement of methane emissions from coal mines is studied in this manuscript. It has been demonstrated that this technique is very useful for the abatement, and even upgrading, of these emissions. However, the effect of humidity on the reactor performance has not been addressed yet, in spite of being well known that water is an inhibitor in catalytic combustion. Experimental studies in a lab-scale isothermal fixed bed reactor demonstrated that water decreases the activity of a palladium on alumina catalyst for the combustion of methane, but this inhibition is entirely reversible, results fitting well to a Langmuir–Hinshelwood kinetic model. Then, the influence of water was studied in a bench-scale RFR operating at near adiabatic conditions at different switching times (100–600 s) and methane feed concentrations (2700–7200 ppm). Finally, a mathematical model for the reverse flow reactor, including the kinetic model with water inhibition, has been validated using the experimental results. This model is of key importance for designing this type of reactors for the treatment of mine ventilation emissions

Dinamika Reaktor Katalitik Aliran Bolak-Balik untuk Oksidasi Emisi Gas Metana

The performance of a reverse flow reactor (RFR) is strongly influenced by the switching time used to alternate the flow direction. This research aimed to study the effect of the switching time on reactor dynamics including the heat propagation along the bed and reaction rate in the oxidation methane for low concentration using catalytic reverse flow reactor. The experimental results show that the reverse flow operating mode can influence heat propagation along the reactor and reaction conversion. Based on the three switching times was tested, the temperature dynamics formed were in the sliding regime. The effect of switching time on RFR on conversion is very significant. When compared to steady operation, RFR operation provides the highest conversion at smaller switching times. At large switching times, the effect of reversal of flow direction becomes less dominant and reactor behavior approaches steady state

Prediction of Methanol Production in a Carbon Dioxide Hydrogenation Plant Using Neural Networks

[EN] The objective of this research was to design a neural network (ANN) to predict the methanol flux at the outlet of a carbon dioxide dehydrogenation plant. For the development of the ANN, a database was generated, in the open-source simulation software "DWSIM", from the validation of a process described in the literature. The sample consists of 133 data pairs with four inputs: reactor pressure and temperature, mass flow of carbon dioxide and hydrogen, and one output: flow of methanol. The ANN was designed using 12 neurons in the hidden layer and it was trained with the Levenberg-Marquardt algorithm. In the training, validation and testing phase, a global mean square (RMSE) value of 0.0085 and a global regression coefficient R of 0.9442 were obtained. The network was validated through an analysis of variance (ANOVA), where the p-value for all cases was greater than 0.05, which indicates that there are no significant differences between the observations and those predicted by the ANN. Therefore, the designed ANN can be used to predict the methanol flow at the exit of a dehydrogenation plant and later for the optimization of the system.Chuquin-Vasco, D.; Parra, F.; Chuquin-Vasco, N.; Chuquin-Vasco, J.; Lo-Iacono-Ferreira, VG. (2021). Prediction of Methanol Production in a Carbon Dioxide Hydrogenation Plant Using Neural Networks. Energies. 14(13):1-18. https://doi.org/10.3390/en14133965S118141

Fluidic valve for reactor regeneration flow switching

An unusual and in many respects advantageous no-moving-part valve is described,developed for switching fluid flows in a through-flow reactor that requires a periodic regeneration by temporary replacement of the process fluid by another, regeneration fluid. The unusual feature of the valve is that it is axisymmetric, built integrally into the inlet part of the reactor body. The valve operation is based upon a monostable axisymmetric variant of the Coanda effect of jet attachment to a wall. The jet is annular and there are two attachment walls of conical shape. The outer hollow cone is dominant while the auxiliary inner convex cone is small, almost vestigial. Concentrating on the performance in a no-spillover regime, experimental data obtained in cold-air laboratory tests using a full-scale model are compared with numerical flowfield computations, using unusual non-dimensional presentation