Investigation of process dynamics and control of polystyrene batch reactor using hybrid model

The effects of operating conditions such as initiator and monomer concentration as well as reactor temperature of polymerization reactors are studied in this work. A recently developed hybrid model for polystyrene batch reactor is utilized in simulation study. The simulation results reveal the sensitivity of polymer properties and monomer conversion to variation of process operating conditions. In the second phase of this study, the optimization problem involving minimum time optimal temperature policy is considered for control study. An advanced neural network-based model predictive controller (NN-MPC) is designed and tested online. The experimental studies reveal that the developed controller is able to track the optimal setpoint with a minor oscillation and overshoot.

Performance analysis of two advanced controllers for polystyrene polymerization in batch reactor

The performance of two advanced model based non-linear controllers is analyzed for the optimal setpoint tracking of free radical polymerization of styrene in batch reactors. Artificial neural network-based model predictive controller (NN-MPC) and generic model controller (GMC) are both applied for controlling the system. The recently developed hybrid model [1] as well as available literature models are utilized in the control study. The optimal minimum temperature profiles are determined based on Hamiltonian maximum principle. Different types of disturbances are artificially generated to examine the stability and robustness of the controllers. The experimental studies reveal that the performance of NN-MPC is superior over that of GMC.

Control of polystyrene batch reactor using fuzzy logic controller

Control of polymerization reactors is a challenging issue for researchers due to the complex reaction mechanisms. A lot of reactions occur simultaneously during polymerization. This leads to a polymerization system that is highly nonlinear in nature. In this work, a nonlinear advanced controller, named fuzzy logic controller (FLC), is developed for monitoring the batch free radical polymerization of polystyrene (PS) reactor. Temperature is used as an intermediate control variable to control polymer quality, because the products quality and quantity of polymer are directly depends on temperature. Different FLCs are developed through changing the number of fuzzy membership functions (MFs) for inputs and output. The final tuned FLC results are compared with the results of another advanced controller, named neural network based model predictive controller (NN-MPC). The simulation results reveal that the FLC performance is better than NN-MPC in terms of quantitative and qualitative performance criterion.

Performance analysis of three advanced controllers for polymerization batch reactor: an experimental investigation

The performances of three advanced non-linear controllers are analyzed for the optimal set point tracking of styrene free radical polymerization (FRP) in batch reactors. The three controllers are the artificial neural network-based MPC (NN-MPC), the artificial fuzzy logic controller (FLC) as well as the generic model controller (GMC). A recently developed hybrid model (Hosen et al., 2011a. Asia-Pac. J. Chem. Eng. 6(2), 274) is utilized in the control study to design and tune the proposed controllers. The optimal minimum temperature profiles are determined using the Hamiltonian maximum principle. Different types of disturbances are introduced and applied to examine the stability of controller performance. The experimental studies revealed that the performance of the NN-MPC is superior to that of FLC and GMC. © 2013 The Institution of Chemical Engineers.

Prediction interval-based neural network modelling of polystyrene polymerization reactor - a new perspective of data-based modelling

In this paper, prediction interval (PI)-based modelling techniques are introduced and applied to capture the nonlinear dynamics of a polystyrene batch reactor system. Traditional NN models are developed using experimental datasets with and without disturbances. Simulation results indicate that traditional NNs cannot properly handle disturbances in reactor data and demonstrate a poor forecasting performance, with an average MAPE of 22% in the presence of disturbances. The lower upper bound estimation (LUBE) method is applied for the construction of PIs to quantify uncertainties associated with forecasts. The simulated annealing optimization technique is employed to adjust NN parameters for minimization of an innovative PI-based cost function. The simulation results reveal that the LUBE method generates quality PIs without requiring prohibitive computations. As both calibration and sharpness of PIs are practically and theoretically satisfactory, the constructed PIs can be used as part of the decision-making and control process of polymerization reactors. © 2014 The Institution of Chemical Engineers.

Improving the quality of prediction intervals through optimal aggregation

Neural networks (NNs) are an effective tool to model nonlinear systems. However, their forecasting performance significantly drops in the presence of process uncertainties and disturbances. NN-based prediction intervals (PIs) offer an alternative solution to appropriately quantify uncertainties and disturbances associated with point forecasts. In this paper, an NN ensemble procedure is proposed to construct quality PIs. A recently developed lower-upper bound estimation method is applied to develop NN-based PIs. Then, constructed PIs from the NN ensemble members are combined using a weighted averaging mechanism. Simulated annealing and a genetic algorithm are used to optimally adjust the weights for the aggregation mechanism. The proposed method is examined for three different case studies. Simulation results reveal that the proposed method improves the average PI quality of individual NNs by 22%, 18%, and 78% for the first, second, and third case studies, respectively. The simulation study also demonstrates that a 3%-4% improvement in the quality of PIs can be achieved using the proposed method compared to the simple averaging aggregation method.

Development and characterization of the 2,4,6-trichlorophenol (2,4,6-TCP) aerobic degrading granules in sequencing batch airlift reactor

2,4,6-trichlorophenol (2,4,6-TCP) aerobic degrading granules were successfully developed in the sequencing batch airlift reactor. The key strategy used in cultivation of the granules was dosing glucose and acetate as co-substrates. After granulation, average concentrations of 2,4,6-TCP and COD in the effluent were less than 8mgL-1 and 59mgL-1, respectively. The removal efficiencies of 2,4,6-TCP and COD were above 93% and 90%, respectively. The specific degradation rate of 2,4,6-TCP peaked at 61mg 2,4,6-TCP gVSS-1h-1 when inoculated at the concentration of 400mgL-1. The extracellular polymeric substance (EPS) contents of the 2,4,6-TCP aerobic degrading granules were decreased compared with the contents in seed sludge. Two peaks attributed to the protein-like fluorophores were identified by three-dimensional excitation emission matrix (EEM) fluorescence spectra. The decrease of fluorescence parameters, e.g., peak locations, intensities, indicated quenching effect of 2,4,6-TCP on the EPS fluorescence. Meanwhile, the shift of peak position indicated chemical changes of the EPS.

Enhancement of aerobic granulation by zero-valent iron in sequencing batch airlift reactor

This study elucidates the enhancement of aerobic granulation by zero-valent iron (ZVI). A reactor augmented with ZVI had a start-up time of aerobic granulation (43 days) that was notably less than that for a reactor without augmentation (64 days). The former reactor also had better removal efficiencies for chemical oxygen demand and ammonium. Moreover, the mature granules augmented with ZVI had better physical characteristics and produced more extracellular polymeric substances (especially of protein). Three-dimensional-excitation emission matrix fluorescence showed that ZVI enhanced organic material diversity. Additionally, ZVI enhanced the diversity of the microbial community. Fe(2+) dissolution from ZVI helped reduce the start-up time of aerobic granulation and increased the extracellular polymeric substance content. Conclusively, the use of ZVI effectively enhanced aerobic granulation.

Prediction interval-based modelling of polymerization reactor: a new modelling strategy for chemical reactors

Precise and reliable modelling of polymerization reactor is challenging due to its complex reaction mechanism and non-linear nature. Researchers often make several assumptions when deriving theories and developing models for polymerization reactor. Therefore, traditional available models suffer from high prediction error. In contrast, data-driven modelling techniques provide a powerful framework to describe the dynamic behaviour of polymerization reactor. However, the traditional NN prediction performance is significantly dropped in the presence of polymerization process disturbances. Besides, uncertainty effects caused by disturbances present in reactor operation can be properly quantified through construction of prediction intervals (PIs) for model outputs. In this study, we propose and apply a PI-based neural network (PI-NN) model for the free radical polymerization system. This strategy avoids assumptions made in traditional modelling techniques for polymerization reactor system. Lower upper bound estimation (LUBE) method is used to develop PI-NN model for uncertainty quantification. To further improve the quality of model, a new method is proposed for aggregation of upper and lower bounds of PIs obtained from individual PI-NN models. Simulation results reveal that combined PI-NN performance is superior to those individual PI-NN models in terms of PI quality. Besides, constructed PIs are able to properly quantify effects of uncertainties in reactor operation, where these can be later used as part of the control process. © 2014 Taiwan Institute of Chemical Engineers.

Degradation of VOCs by phototcatalytic oxidation reactor using TiO2 pellets

Volatile Organic Compounds (VOCs) are air pollutants that come from burning fossil fuels and industrial emissions. They have potentially adverse health effects being carcinogenic and highly persistent in the environment. The use of photocatalytic oxidation to remove VOCs has the potential to be applied in indoor air quality improvement and industrial emission control. A fixed bed photocatalytic reactor was designed and built. UV black light lamps were installed in the reactor to provide a source of UV radiation. A non-film titania media as pellets were placed on the three fixed beds within the reactor. Toluene and acetone were used as indicators of VOCs during the experiment. With a flow rate of 12.75l/min, the oxidation efficiencies were obtained at four different concentrations of acetone laden gas streams ranging from 40ppm to 250ppm. It was found that the lower the acetone concentration of the untreated inlet gas, the higher the oxidation efficiency. The oxidation efficiency was in the range of 40–70% for various concentrations of untreated gases. Two concentrations of toluene laden gas stream were also tested using the same reactor. The oxidation efficiencies were found as 50% for 120ppm toluene gas and 45% for 300ppm toluene gas. It was found that the times required for toluene to reach oxidization equilibrium have been halved than for acetone gas stream. Other parameters such as flow rate and UV intensity were also altered to see their effects on the oxidation efficiency. A full spectrum scan was carried out using a Bio-rad Infrared spectrometer. It was found that the main components of the treated gas stream from the outlet of the reactor were CO2 and water along with small amount of untreated acetone. The suspected intermediates of aliphatic hydrocarbons and CO were found in very minimal amounts or undetectable. The research experiments supported that the TiO2 pellets can work effectively in a fixed bed photocatalytic reactor and achieve significant oxidation efficiencies for degradation of toluene and acetone as indicators of VOCs.

Photocatalytic decolorization of lanasol blue CE dye solution using a flat-plate reactor

This paper reports the effectiveness of the photocatalysis TiO₂ in degrading Lanasol Blue CE. A flat-plate reactor (FPR) with a reactor area of 0.37 m² and ultraviolet (UV) light source of six 36 W blacklight lamps was used in the study. Operating variables including dosage of the photocatalyst, flow rates through the FPR, UV intensity, and tilted angle of the reactor were investigated to degrade Lanasol Blue CE. Results showed that the photocatalytic process can efficiently remove the color in textile dyeing effluent. The degradation process was approximated using first-order kinetics. The photocatalytic apparent reaction rate increased with the increasing UV intensity received by the photocatalyst TiO₂ in slurry. The liquid flow rate and tilted angle influenced the film thickness. The apparent reaction rate constant was mainly determined by the liquid film thickness, UV intensity, and the dosage of the photocatalyst. The findings of this research can be utilized as preliminary input for potential solar photocatalytic applications on color removal from dye solutions.

Photocatalytic degradation of dyes in wastewater using TiO2/UV in a flat-plate reactor

The photocatalyst TiO₂ with UV irradiation was used to degrade dyes in textile effluent in a flat-plate photoreactor. A test system was built with the reactor area of 1 x 0.3m2, UV light of six 36W-blacklight. TiO₂ powder P25 with BET surface area 50±15m2/g, average primary particle size 21 nm, purity> 99.5% and content of 83.9% anatase and 16.1 % rutile was used as the photocatalyst. A number of dyes commonly present in dyeing wastewater were tested in this study. The different operating parameters, such as dosage of photocatalyst, the structure of the reactor, flow rates through the flat-plate reactor, UV radiation intensity and tilted angle of the reactor, were investigated. The results showed that the photocatalytic process could efficiently remove most of the colour contained in the dyeing wastewater. It was experimentally observed that first-order kinetics was adequate for characterising the process. The flow rate and the tilted angle had some influence on the film thickness of the fluid in the reactor and the empirical correlation between the film thickness of the fluid and these two parameters was developed. The photoreaction rate was mainly determined by the film thickness of the fluid on the reactor surface and the dosage of the photocatalyst. Optimum operating parameters of the system were found to be at the film thickness of about 1.4mm and a TiO₂ dosage of 1 gIL. The higher the UV intensity, the faster the reaction rate was. The results of these experiments showed that this method has the great potential for colour removal from wastewater at commercial scale.

To overcome the common difficulty of separating the used TiO₂ suspension after treatment precipitation followed with filtration was used in this study to determine the separation efficiencies. On the other hand, TiO₂ in a small pillar shape was also studied for photocatalytic degradation of textile dye effluent. The pillar pellet was made in Oegussa Company, Germany ranging from 2.5 to 5.3mm long and with a diameter of 3.7mm. It was almost pure TiO₂ (83.2% anatase and 16.8% rutile), with a S-content of <20 ppm and a CI content of the order of 0.1 wt. %. No further elements are present in contents above 0.05 wt.%. The TiO₂ pillars were placed on the flat-plate reactor that was divided by the rectangular slots and irradiated under UV light when the treated solution went through the reactor. Four dyes and their mixtures were tested. The results showed that the photocatalytic process under this configuration efficiently remove the colour from textile dyeing effluent, and pillar shape TiO₂ photocatalyst was not dissolved in water and very easy to be separated from solution, enabling it to be reused many times. The first-order kinetics was adequate for characterising the photocatalytic degradation process and the photocatalytic performance was comparable to TiO₂ powder. It is believed that the TiO₂ pellet would be a preferable form of photocatalyst in applications for textile effluent treatment process, and other wastewater treatment processes.

Solar photocatalytic oxidation (PCO) process with a flat-plate reactor

An experimental rig with a flat-plate solar reactor was built to study the effectiveness of degradation using the reactive methylene blue as sensitive objective. The factors that affect the degradation performance, such as dosages of photocatalyst (Ti0₂), initial concentration of reactive methylene blue, flow rate through the flat-plate reactor, solar UV radiation intensity and decolourising efficiency of the solution, have been investigated. The results showed that the solar PCO process with a Flat-plate Reactor could degrade the methylene blue and decolour in methylene blue solution efficiently.

Simulation-based support tool for operator performance in batch manufacturing

In industry, the workload and utilization of shop floor operators is often misunderstood. In this paper, we will present several real case studies, using Discrete Event Simulation (DES) models, which allow us to better understand operators in a batch manufacturing environment. The first study investigates labour in a machining plant consisting of multiple identical CNC machines that batch produce parts. The second study investigates labour in an eight station, gravity die casting rotary table. The results from these studies have shown that there can be potential improvements made by the production planners in the current labour configuration. In the first case study, a matrix is produced that estimates what the operator's utilization levels will be for various configurations. From this, the preferred operator to machine ratio over a range of cycle times is presented. In the second study, the results have shown that by reducing the casting cycle time, the operator would be overloaded. A discrete event simulation of these two cases highlighted areas that were misunderstood by plant management, and provided them with a useful decision support tool for production planning.

A decision support tool for resource allocation in batch manufacturing

A decision support tool for production planning is discussed in this paper to perform the job of machine grouping and labour allocation within a machining line. The production plans within the industrial partner have been historically inefficient because the relationship between the cycle times, the machine group size, and the operator's utilisation hasn't been properly understood. Starting with a simulation model, a rule-base has been generated to predict the operator's utilisation for a range of production settings. The resource allocation problem is then solved by breaking the problem into a series of smaller sized tasks. The objective is to minimise the number of operators and the difference between the maximum and minimum cycle times of machines within each group. The results from this decision support tool is presented for the particular case study.