Industrial Batch Reactor Research Articles

Microreactor x millireactor: Experimental performance in the epoxidation of soybean oil

In the Chemical Reaction Engineering (CRE) field, the concept of Process Intensification (PI) can be applied using microreactors and millireactors, i.e., tubular reaction devices with reduced dimensions, compared with typical reactors used industrially. This concept was explored in the present study by the conduction of the reaction system for the epoxidation of soybean oil without catalysts and droplet stabilizers. The biphasic reaction system generates the epoxidized soybean oil (ESO), a promising compound in the plasticizers field that follows Green Chemistry principles. Besides the effect of the equipment itself (microreactor or millireactor), the effects of the temperature and the mean residence time were also explored in this study. The results confirmed the intensification of the epoxidation process, particularly in the microreactor, due to the strong dependence of the reactions on mass and heat transfer effects. This led to significantly lower reaction times, in the order of minutes, comparatively to the typical reaction times of hours employed in industrial batch and fed-batch reactors, with the achievement of the most promising results of 19.77 g I2/100 g for the iodine index and 5.67 g O/100 g for the oxirane index, values corresponding to 84.2% of double bond conversion and 74.9% of epoxy yield.

Direct-simulation Monte Carlo modeling of reactor-scale gas-dynamic phenomena in a multiwafer atomic-layer deposition batch reactor

Atomic layer deposition (ALD) using multiwafer batch reactors has now emerged as the manufacturing process of choice for modern microelectronics at a massive scale. Stringent process requirements of thin film deposition uniformity within wafer (WiW) and wafer–wafer (WTW) in the batch, film conformity along submicrometer wafer features, thin film quality, and the utilization of expensive precursors in the reactor dictate ALD reactor design and process parameter optimization. This paper discusses a particle-based direct-simulation Monte Carlo (DSMC) of the full reactor scale simulation that overcomes the low Knudsen number limitation of typical continuum computational fluid dynamics approaches used for modeling low-pressure ALD reactors. A representative industrial multiwafer batch reactor used for the deposition of Si-based thin films with N2 and Si2Cl6 (hexachlorodisilane) as process feed gases with pressures in the range 43–130 Pa and a uniform reactor temperature of 600 °C is simulated. The model provides detailed insights into the flow physics associated with the transport of the precursor species from the inlets, through wafer feed nozzles, into the interwafer regions, and finally through the outlet. The reactor operating conditions are shown to be in the slip/transitional flow regime for much of the reactor volume and especially the feed gas nozzle and interwafer regions (where the Knudsen number approaches ∼0.2), justifying the need for a high-Knudsen number DSMC approach as in this work. For the simulated conditions, the nonuniformity of precursor species immediately above the wafer surface is predicted to be within <1% for a given wafer and <2% across the entire multiwafer stack. Results indicate that higher pressure degrades WiW and WTW uniformity. A reactor flow efficiency is defined and found to be ∼99%, irrespective of the chamber pressure.

Selective catalytic hydrogenation of the triple bond in acetylenic alcohols in a microcapillary reactor for fine organic synthesis

The purpose of this work is to present our latest achievements in the design, synthesis and development of a new catalytic microreactor for the processes of selective and continuous synthesis of substances with added value that are used in fine organic synthesis and pharmaceutical industries. In this advanced device, metal nanoparticles are embedded in mesoporous materials, which are deposited in the form of highly active and selective catalytic films on the walls of capillary microchannels with diameters of 220 and 530 μm. In such microreactor systems, there is no subsequent separation of the catalyst, which facilitates continuous operation. Potentially the most stable coatings are crystalline or amorphous titanium dioxide based metal oxides synthesized by the sol-gel method and containing embedded catalytically active polymetallic nanoparticles. The catalytic properties and operational stability of capillary microreactors were investigated at atmospheric pressure and compared with conventional industrial batch reactors. The developed Pd50Zn50 /TiO2 coating showed a higher activity (1.5 gMBE·gPd–1·s–1) and selectivity (96.7 %) in comparison with the coatings described earlier in the literature and with the industrial Lindlar catalyst, and retained high catalytic performance after 88 hours of reaction.

Reactor and Product Optimization via Raman Fiber Optics Monitoring: Application to Polymer‐Based Proppants

AbstractA setup to characterize polymerization kinetics of polymer‐based proppants produced in an industrial batch reactor by suspension polymerization is presented. A microscale reactor is designed to mimic temperature and pressure conditions of the industrial counterpart. Raman spectroscopy is used to follow the consumption of vinyl bonds of the styrene monomer and the crosslinker via disappearance of the peak at 1632 cm‐1. Raman data from the microscale reactor are remotely obtained via a fiber optics system. Reaction progress by any generic formulation can be safely followed up to conversions of 90%, well beyond the gel point. Reaction rates are used to define feasible temperature–time profiles for the industrial reactor. In parallel, bulk and suspension polymerizations are carried out under those temperature–time profiles in a 3 L laboratory reactor to produce proppants formulations with the geometry required to perform product characterization, mainly focused on the thermal and mechanical response of the polymer particles. Overall, the whole setup allows optimization of proppant formulations and the cost of their processes of production.

Modeling of batch production of high vinyl styrene/butadiene copolymers for high performance tires

AbstractMathematical modeling of batch production of high vinyl random styrene/butadiene copolymers in an industrial batch reactor was accomplished. Mass and energy balance equations were solved to model the nonisothermal anionic copolymerization of styrene/butadiene performed in an industrial batch reactor. The simulation model allows to predict the variation of the individual and global monomer conversion(s), temperature, pressure, and volume level of reactor with the time. Simulation model was used to determine the optimal polymerization conditions to maximize the production of a high vinyl random styrene/butadiene copolymer used in the manufacture of high performance tires.

Robust optimization of solid-liquid batch reactors under parameter uncertainty

Unknown parameters in batch process models can be estimated as uncertainties owing to model mismatch, measurement errors and even the absence of variable measurements. Applying a model-based dynamic optimization with the estimated parameter values is likely to violate path and end-point constraints. This work aims at developing a robust off-line optimization strategy to overcome model uncertainty and improve control feasibility. First, model parameter uncertainty is quantified by the likelihood confidence region from parameter estimation. A two-stage multi-scenario approach is performed to enhance robustness of optimal solutions by adding worst-case scenarios to the optimal control problem. Detailed formulations of two stages for batch process optimal control under multiple scenarios and feasibility evaluation over the uncertainty space are introduced. Additionally, to avoid local solutions at the feasibility stage, we develop a multi-start approach to solve nonlinear programming problems starting from extreme points on the hyper-elliptical confidence region. This robust optimization approach is applied to case studies of industrial solid-liquid batch reactors. The results indicate the proposed approach is able to enhance robustness of optimal control and prevent hard constraint violations within a few iterations.

Dissolved oxygen control strategies for the industrial sequencing batch reactor of the wastewater treatment process in the papermaking industry

The fuzzy-PID dissolved oxygen control strategy is superior in control performance, energy consumption, and anti-interference for the SBR process of papermaking mills.

Lyapunov exponents with Model Predictive Control for exothermic batch reactors

Thermal runaways cause significant safety issues and financial loss for industrial batch reactors due to the disruption of normal operation. The intensification of processes is restricted, since control systems are not capable of detecting stability boundaries of the system and hence are overly conservative. For this purpose Lyapunov exponents are introduced as a stability criterion. It is shown that Lyapunov exponents can correctly predict the stability of batch reactor systems. This stability criterion is embedded in Model Predictive Control, which results in a novel control scheme. This scheme allows the controlled increase of the reaction temperature to achieve a target conversion in a reduced completion time of reaction.

Modeling and simulation of the industrial sequencing batch reactor wastewater treatment process for cleaner production in pulp and paper mills

Being an internationally accepted standard for the activated sludge modeling, the Activated Sludge Model No.1 (ASM1) was used to simulate the treatment of paper mills effluent in an industrial full-scale sequencing batch reactor (SBR). Key characteristic parameters were estimated and corrected with the wastewater temperature: maximum heterotrophic growth rate μH (9.69/day), heterotrophic yield YH (0.625 g cell COD/g COD oxidized) and heterotrophic decay rate bH (1.98/day). The wastewater chemical oxygen demand (COD) was fractionated as slowly biodegradable substrate XS (20%), particulate inert organic matter XI (58%), readily biodegradable substrate SS (18%) and soluble inert organic matter SI (4%). Finally, the SBR operation was simulated with field data from the paper mill. The average relative error of the simulated effluent COD was 12.7%. The results showed that the ASM1 could be reasonably used in the papermaking wastewater treatment simulation.

Numerical study on increasing PVC suspension polymerization productivity by using PSO optimization algorithm

Despite the high application of poly (vinyl chloride) (PVC), it is still produced by batch wise rectors via suspension polymerization. Therefore, it is essential to improve the productivity of these processes by decreasing in the time of each batch maintaining desired polymer quality. The industrial batch reactors are restricted by a maximum reactor cooling capacity limiting the extent of rate of heat release over the exothermic polymerization. The reactor cooling system was fixed at maximum cooling capacity to satisfy an isothermal process. In order to reduce the polymerization time, the polymerization rate at any time of the batch should be in its possible maximum value. An operation approach is to run the industrial process isothermally using a mixture (cocktail) of initiators with different half-life. The aim of this study is to apply a mathematical model for PVC polymerization process for a choosing the optimal amounts of initiators in the cocktail by using particle swarm optimization algorithm. Results show that if an optimal amount of different initiators in the mixture was chosen, a significant reduction of the total processing time for a prescribed polymer specification can be obtained, as compared to the case in which only one initiator is optimally chosen.

“Zero-charge” SiO2/Al2O3 stacks for the simultaneous passivation of n+ and p+ doped silicon surfaces by atomic layer deposition

To achieve high conversion efficiencies, advanced silicon solar cell architectures such as interdigitated back contact solar cells demand that defects at both the n+ and p+ doped Si surfaces are passivated simultaneously by a single passivation scheme. In this work, corona charging experiments show that the fixed charge density Qf is a key parameter governing the passivation of both surface types. Alternatively, Qf can be controlled from strongly negative to even positive values by carefully tuning the SiO2 interlayer thickness in SiO2/Al2O3 stacks prepared by atomic layer deposition (ALD). This control in Qf allows for a superior passivation of n+ Si surfaces by SiO2/Al2O3 stacks compared to a single layer Al2O3. For instance, for SiO2 interlayer thicknesses of ~3–14nm, the recombination parameter of an n+ Si surface having a high surface doping concentration Ns of 2×1020cm−3 was reduced from J0n+=81fA/cm2 to J0n+=50fA/cm2. Simulations predict that the SiO2/Al2O3 stacks outperform Al2O3 passivation layers particularly on n+ Si surfaces having a moderate Ns in the range of 1018–1020cm−3. On p+ Si surfaces, J0p+≤54fA/cm2 was achieved for all ALD SiO2 interlayer thicknesses investigated (i.e., 1–14nm). The SiO2/Al2O3 stacks presented in this work are compatible with SiNx capping and subsequent high-temperature firing steps, which are typically used in solar cell processing. Furthermore, the results were successfully reproduced in an industrial ALD batch reactor using a low-temperature process. This makes ALD SiO2/Al2O3 stacks a promising candidate for the simultaneous passivation of n+ and p+ Si surfaces in solar cells.

A novel process for the synthesis of unsaturated polyester

Traditionally polyester production is done in a batch reactor equipped with a separation column for a batch distillation. A promising alternative for the intensification of this process is reactive distillation. The aim of this paper is to study the conceptual design of reactive distillation and to find out whether reactive distillation is potentially interesting compared to batch reactor process. Therefore, a reactive distillation model is developed and sensitivity analysis is used to obtain the design and operational parameters for the reactive distillation process. These parameters are the required number of stages, required residence time, feed ratio, reflux ratio and temperature of feed stream. The model predicts the polymer attribute, isomerization and saturation composition of the polymer in the range of industrial polyester production data. The simulation study shows that the total production time of polyester in a continuous reactive distillation system is reduced from 12 h to 1.5–2 h compared to the industrial batch reactor process. The equilibrium conversion is also raised by 7% compared to the conventional process. The model demonstrated that reactive distillation has the potential to intensify the process by factor of 6–8 in comparison to the batch reactor.

Model based control of a liquid swelling constrained batch reactor subject to recipe uncertainties

This work presents the application of nonlinear model predictive control (NMPC) to a simulated industrial batch reactor subject to safety constraint due to reactor level swelling, which can occur with relatively fast dynamics. Uncertainties in the implementation of recipes in batch process operation are of significant industrial relevance. The paper describes a novel control-relevant formulation of the excessive liquid rise problem for a two-phase batch reactor subject to recipe uncertainties. The control simulations are carried out using a dedicated NMPC and optimization software toolbox OptCon which implements efficient numerical algorithms. The open-loop optimal control problem is computed using the multiple-shooting technique and the arising nonlinear programming problem is solved using a sequential quadratic programming (SQP) algorithm tailored for large-scale problems, based on the freeware optimization environment HQP. The fast response of the NMPC controller is guaranteed by the initial value embedding and real-time iteration technologies. It is concluded that the OptCon implementation allows small sampling times and the controller is able to maintain safe and optimal operation conditions, with good control performance despite significant uncertainties in the implementation of the batch recipe.

Computer aided design and operation of industrial poly(vinyl chloride) batch suspension polymerisation reactors

A comprehensive multiscale, multiphase dynamic model is developed to simulate heterogeneous vinyl chloride (VCM) suspension polymerisation in industrial batch reactors. From the numerical solution of the proposed integrated model, the evolution of the molecular (i.e. molecular weight distribution, long chain branching, short chain branching, terminal double bonds) and morphological (i.e. particle size distribution, porosity) polymer properties in a PVC batch suspension polymerisation reactor can be predicted. In particular, the polymer molecular properties are determined by employing a kinetic mechanism which describes the VCM free radical polymerisation in both monomer and polymer rich phases. Semi-empirical and phenomenological expressions are used to describe the breakage and coalescence rates of dispersed monomer droplets in terms of the type and concentration of suspending agent, quality of agitation and evolution of the physical, thermodynamic and transport properties of the polymerisation system. The dynamic discretised particle population balance equation (PBE) is solved to calculate the dynamic evolution of the particle size distribution of the produced PVC. Furthermore, the primary particle size distribution (PPSD) inside the polymerising monomer droplets, which influences to a large degree the porosity of the final PVC grains, is determined by the solution of a PBE governing the nucleation, growth and aggregation of the primary particles. Finally, dynamic mass and energy balances are derived to assess the dynamic behaviour of the PVC batch suspension polymerisation reactors. The theoretical model predictions show good agreement with a comprehensive series of experimental data provided by the PVC industry. This verification of the proposed model makes it a powerful tool for the simulation of large scale PVC batch suspension polymerisation reactors.

Nonlinear model predictive control of an industrial batch reactor subject to swelling constraint

This paper presents the application of nonlinear model predictive control (NMPC) to a simulated industrial batch reactor subject to safety and productivity constraints due to swelling. The catalyst used in the chemical process decomposes in the reactor; therefore it is fed in discrete time steps during the batch. Although the optimal reactor temperature profile, using a fixed catalyst dosing policy, is optimized off-line an on-line control solution is needed in order to accommodate the reaction rate and level disturbances which arise due to catalyst dosing uncertainty (feeding time and mass). The on-line control method is based on the shrinking horizon optimal control methodology and it uses a reaction and hydrodynamic model. It is concluded that the implemented shrinking horizon on-line optimization strategy is able to calculate the optimal temperature profile without causing level swelling.

Batch reactor optimization under liquid swelling safety constraint

This article presents the modeling and optimization of an industrial batch reactor operation with regard to production safety constraint which is represented by the reactor content swelling. Swelling occurs due to the evacuation of a coupled product which passes through the liquid in order to reach the reaction mass surface. In order to model the reaction system a chemical model linked to a hydrodynamic model is developed. The goal of the optimization is to calculate the temperature and pressure profiles which maximize conversion rates, without exceeding the maximum reactor level. The optimal temperature profile is calculated using the optimal control optimization method and the results show that a productivity increase of up to 36% can be achieved as compared to the industrial process.

CFD simulation of accidents in industrial batch stirred tank reactors

In this work, we have analyzed the application of CFD (computational fluid dynamics) to simulate consequences of stirrer failures, which can lead to reactor thermal runaway. For a typical industrial batch reactor, several “CFD computational experiments” have been carried out to explore runaway and non-runaway situations. Then the results of the CFD simulations, supported by an on-line divergence criterion, have been used to detect a dangerous reactor behavior. It has been found out, that even for the reactor operated at potentially safe conditions a failure of the stirring system can lead to serious thermal runaways. A significance of a proper location of temperature probe for an early runaway detection has also been discussed. A modification of the applied divergence criterion to avoid false alarms has been proposed and tested.

Modeling of a Three-Phase Industrial Batch Reactor Using a Hybrid First-Principles Neural-Network Model

Modeling of a Three-Phase Industrial Batch Reactor Using a Hybrid First-Principles Neural-Network Model

Evaluation study of an efficient output feedback nonlinear model predictive control for temperature tracking in an industrial batch reactor

The paper illustrates the benefits of nonlinear model predictive control (NMPC) for the setpoint tracking control of an industrial batch polymerization reactor. Real-time feasibility of the on-line optimization problem from the NMPC is achieved using an efficient multiple shooting algorithm. A real-time formulation of the NMPC that takes computational delay into account is described. The control relevant model for the NMPC is derived from the complex-first principles model and is fitted to the experimental data using maximum likelihood estimation. A parameter adaptive extended Kalman filter (PAEKF) is used for state estimation and on-line model adaptation. The performance of the NMPC implementation is assessed via simulation and experimental results.

Incorporating Prior Knowledge in a Cubic Spline ApproximationApplication to the Identification of Reaction Kinetic Models

Data smoothing and resampling are often necessary in the handling of data obtained from laboratory and industrial experiments. This paper presents a new algorithm for incorporating prior knowledge into the spline smoothing of interrelated multivariate data. Prior knowledge based on visual inspection of the variables and/or knowledge about the assumed balance equations can be transformed into linear equality and inequality constraints on the parameters of the splines. The splines can then be simultaneously identified from the available data by solving one quadratic programming problem. To demonstrate the applicability of this method, two examples are given. In the first example, the proposed approach is applied to the identification of the kinetic parameters of a simulated reaction network, whereas in the second example, data taken from an industrial batch reactor are analyzed. The results show that, when the proposed constrained spline-smoothing algorithm is applied, one achieves not only better fitting to the data points, but also improved performance in the estimation of the kinetic parameters compared to the case in which no prior knowledge is involved.