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.

Advanced microwave-assisted production of hybrid electrodes for energy applications

Carbon nanotubes are one of the most prominent materials in research for creating electrodes for portable electronics. When coupled with metallic nanoparticles the performance of carbon nanotube electrodes can be dramatically improved. Microwave reduction is an extremely rapid method for producing carbon nanotube-metallic nanoparticle composites, however, this technique has so far been limited to carbon nanotube soot. An understanding of the microwave process and the interactions of metallic nanoparticles with carbon nanotubes have allowed us to extend this promising functionalisation route to pre-formed CNT electrode architectures. Nanoparticle reduction onto pre-formed architectures reduces metallic nanoparticle waste as particles are not formed where there is insufficient porosity for electrochemical processes. A two-fold increase in capacitive response, stable over 500 cycles, was observed for these composites, with a maximum capacitance of 300 F g−1 observed for a carbon Nanoweb electrode.

Nanostructured carbon electrodes

In its conducting form, carbon has proven to be a versatile, robust and high performing electrode material in areas such as energy conversion, energy storage and even medical bionics. In our laboratories we have been interested in the fabrication and utilization of nanostructured electrodes based on more recently discovered forms of carbon. These include carbon nanotubes and graphene.

General synthesis of two-dimensional patterned conducting polymer-nanobowl sheet via chemical polymerization

A general method for the generation of two-dimensional (2D) ordered, large-area, and liftable conducting polymer-nanobowl sheet has been demonstrated via chemical polymerization for the first time. The sheet is made using the monolayer self-assembled from polystyrene (PS) spheres at the aqueous/air interface as template, followed by depositing conducting polymer on the part of PS monolayer submerging in the aqueous phase via chemical polymerization, and core extraction. During the process of polymerization, no substrate is required, which caused the as-prepared patterned conducting polymer sheet can be easily lifted-off and deposited, in full size, on any flat substrate. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), and Fourier transform infrared (FTIR) spectrum were used to characterize the products

Simultaneous polymerization and crosslinking for the synthesis of molecular-level graphene oxide-polyacryl amide-CeOx composites

Synthesis of molecular-level multiple-component composites are particularly challenging due to the lack of direct bonding among different components. In this study, molecular-level graphene oxide (GO)-polyacryl amide (PAM)-CeOx composites were successfully synthesized, using the simultaneous polymerization and crosslinking strategy. Attenuated total reflection Fourier transform infrared (ATR-FTIR) and nuclear magnetic resonance (NMR) techniques confirmed that polyacryl amide (PAM) chains were successfully grafted onto the surface of GO. X-ray photoelectron spectroscopic (XPS) and X-ray diffraction (XRD) analyses further revealed the characteristic signals of cerium elements and CeO2 phase respectively. Scanning electron microscopy (SEM) showed that the surface morphology of the GO-PAM-CeOx composites was substantially thicker and rougher than those of the original GO. Further exploration of the reaction mechanism clearly demonstrate the existence of strong chelating interaction among PAM chains and Ce(IV) ions. In particular, the polymerization of acryl amide monomers and the crosslinking reaction between PAM and Ce(IV) or Ce(III) ions were realized simultaneously, leading to the final formation of molecular-level GO-PAM-CeOx composites. Moreover, the as-synthesized GO-PAM-CeOx composites were capable of effectively decomposing Rhodamine B under simulated sunlight, making it a potential candidate as a new photo catalyst. To sum up, this report demonstrates the potential utility of simultaneous polymerization and crosslinking method for the synthesis of other multiple-component composites at molecular-level.

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.

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.