Adaptive linear quadratic gaussian (LQG) control of a bioreactor

The paper deals with the modelling and adaptive control of a continuous-flow fermentation process for the production of alcohol. The fermenter model has been developed from mass balance and leads to nonlinear differential equations. In practice, control strategies are difficult to derive using this non-linear model. The dilution rate and the substrate concentration have been considered as control and controlled variables, respectively. The adaptive control algorithm implemented is based on the linear quadratic control approach, where the associated Riccati equation is iterated until the system closed-loop poles belong to a restricted stability domain which is included in the unit circle. A single input/output model is used for control purposes. The model parameters are estimated on-line using a robust identification algorithm which includes: data normalization, time-varying forgetting factor, covariance matrix factorization, etc. Experimental results show the performance of this adaptive scheme and its ability to control biotechnological processes.     

Multivariable process control - A survey

A survey is provided of the state of the art in multivariable process control. Recent literature is organized and tabulated by topic to provide material for further study. The survey encompasses both linear and nonlinear multivariable systems, on-line estimation, adaptive control, distributed systems, and recent developments in computer-aided control system design. These developments together with recent applications provide the basis for predictions of the future evolution of the field.     

Multi-scale segmentation image analysis for the in-process monitoring of particle shape with batch crystallisers

The morphological forms and habits of pharmaceutical crystals are important properties that can be affected by minor changes in operating conditions such as cooling rates and supersaturation. As a result the pharmaceutical industry demands on-line techniques for real-time measurement of the dynamic changes of these properties in crystallisers. On-line imaging represents a potentially powerful technique for real-time monitoring of the morphological forms during crystal growth, but a major challenge is the availability of methods for image analysis that need to be tolerant to the quality of on-line images, accurate, fast and robust. This paper describes a multi-scale segmentation methodology for analysis of images obtained for batch cooling crystallisation of (L)-glutamic acid using an on-line high-speed imaging system developed by the pharmaceutical manufacturer GlaxoSmithKline. The method proves to be able to analyse effectively the on-line images of different crystal morphological forms, and of varied qualities. Application of the methodology to the analysis of images from a different on-line imaging probe and from an off-line slurry sample imaging system demonstrated the capability of generalisation of the method.     

Shape Evolution of a Coherent Tetragonal Precipitate in Partially Stabilized Cubic ZrO2: A Computer Simulation

The kinetics of shape evolution of a tetragonal precipitate coherently embedded in a cubic matrix are examined. Specifically, the morphology of tetragonal ZrO₂ particles in partially stabilized cubic ZrO₂ is discussed. A computer simulation, carried out without any a priori constraint on possible kinetic paths and particle morphologies, shows that a lenslike shape appears during growth of a tetragonal particle. Upon further coarsening , the shape relaxes into a rhombus bounded by facets. Depending on the balance between interfacial and strain energies controlled by the particle size, the facets can be smoothly curved or straight. The predicted particle morphologies are in good agreement with the experimental observations. The kinetic model proposed is quite general for simulating microstructural developments during decomposition involving a crystal lattice symmetry change where elastic strain accommodation plays an important role.     

Recent advances in the monitoring,modelling and control of crystallization systems

Crystallization is one of the most important unit operations used for the separation and purification of crystalline solid products. Appropriate design and control of the crystallization process is paramount to produce crystalline products with tailor-made-properties. This paper provides an overview of selected recent developments in the modelling, monitoring and control of crystallization processes. We consider the topics discussed in this review to be enabling technologies for the development of the next generation of crystallization processes with significantly improved predictability, robustness and controllability.     

Carbon materials and catalytic wet air oxidation of organic pollutants in wastewater

The use of carbon materials as catalytic support or direct catalyst in catalytic wet air oxidation (WAO) of organic pollutants is reviewed. The discussion covers important engineering aspects including the characterisation, activity and stability of carbon catalysts, process performance, reaction kinetics and reactor modelling. Recommendations for further research in catalytic WAO are outlined.     

Imaging droplet freezing using MRI

Magnetic resonance imaging (MRI) is used to provide spatially resolved structural and chemical composition characterisation of droplets undergoing freezing. To this end, MRI is applied to a diameter sucrose solution droplet, suspended in cold air. During the consequential solidification of the droplet, the spatial location of nucleation and the crystal growth of the droplet are followed using non-invasive two dimensional (2D) images; these are produced using the fast MRI technique, RARE. This is able to both quantify crystal growth rates, as well as the unfrozen liquid mass fraction for the optically opaque freezing droplets. Such information is of major interest in the verification of models describing the freezing of such droplets. The spatial re-distribution of the sucrose solute as a consequence of freezing is monitored using MR 1D chemical shift profiling. The formation of a concentrated sucrose layer at the droplet surface was detected.     

Nanosensors: towards morphological control of gas sensing activity. SnO2, In2O3, ZnO and WO3 case studies

Anisotropy is a basic property of single crystals. Dissimilar facets/surfaces have different geometric and electronic structure that results in dissimilar functional properties. Several case studies unambiguously demonstrated that the gas sensing activity of metal oxides is determined by the nature of surfaces exposed to ambient gas. Accordingly, a control over crystal morphology, i.e. over the angular relationships, size and shape of faces in a crystal, is required for the development of better sensors with increased selectivity and sensitivity in the chemical determination of gases. The first step toward this nanomorphological control of the gas sensing properties is the design and synthesis of well-defined nanocrystals which are uniform in size, shape and surface structure. These materials possess the planes of the symmetrical set {hkl} and must therefore behave identically in chemical reactions and adsorption processes. Because of these characteristics, the form-controlled nanocrystals are ideal candidates for fundamental studies of mechanisms of gas sensing which should involve (i) gas sensing measurements on specific surfaces, (ii) their atomistic/quantum chemical modelling and (ii) spectroscopic information obtained on same surfaces under operation conditions of sensors.     

A systematic framework for design of process monitoring and control (PAT) systems for crystallization processes

A generic computer-aided framework for systematic design of a process monitoring and control system for crystallization processes has been developed to study various aspects of crystallization operations. The systematic design framework contains a generic crystallizer modelling toolbox, a tool for generation of the supersaturation set-point for supersaturation control, as well as a tool for design of a process monitoring and control system (also called Process Analytical Technology (PAT) system). This systematic design allows one to generate the necessary problem-chemical system specific model, the necessary supersaturation set-point as well as a PAT system design including implementation of monitoring tools and control strategies in order to produce the desired target product properties notably crystal size distribution (CSD) and shape for a wide range of crystallization processes. Application of the framework is highlighted through a case study involving the design of a monitoring and control system for a potassium dihydrogen phosphate (KDP) crystallization process, where also the one-dimensional CSD and two-dimensional CSD modelling features are highlighted.     

Stereo imaging of crystal growth

Significance A methodology that directly images the full three‐dimensional (3‐D) shape of crystals within a crystallizer is reported. It is based on the mathematical principle that if the two‐dimensional (2‐D) images of an object are obtained from two or more different angles, the full 3‐D crystal shape could be reconstructed. A prototype instrument is built and proof of concept study performed to demonstrate the potentials in using the system for 3‐D measurement of crystal shape and shape distribution. It is our belief that 3‐D measurement of crystal shape represents a significant step forward from existing work of 2‐D measurement of crystal morphology and is potentially of great significance to research toward closed‐loop control of crystal morphology. © 2015 American Institute of Chemical Engineers AIChE J, 62: 18–25, 2016     

Modeling and control of protein crystal shape and size in batch crystallization

In this work, the modeling and control of a batch crystallization process used to produce tetragonal hen egg white lysozyme crystals are studied. Two processes are considered, crystal nucleation and growth. Crystal nucleation rates are obtained from previous experiments. The growth of each crystal progresses via kinetic Monte Carlo simulations comprising of adsorption, desorption, and migration on the (110) and (101) faces. The expressions of the rate equations are similar to Durbin and Feher. To control the nucleation and growth of the protein crystals and produce a crystal population with desired shape and size, a model predictive control (MPC) strategy is implemented. Specifically, the steady‐state growth rates for the (110) and (101) faces are computed and their ratio is expressed in terms of the temperature and protein concentration via a nonlinear algebraic equation. The MPC method is shown to successfully regulate both the crystal size and shape distributions to different set‐point values. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2317–2327, 2013     

A DYNAMIC MODEL WITH ON-LINE IDENTIFICATION FOR ROTARY SUGAR DRYING PROCESSES

A dynamic modelling methodology, which combines on-line variable estimation and parameter identification with physical laws to form an adaptive model for rotary sugar drying processes, is developed in this paper. In contrast to the conventional rate-based models using empirical transfer coefficients, the heat and mass transfer rates are estimated by using on-line measurements in the new model. Furthermore, a set of improved sectional solid transport equations with localized parameters is developed in this work to replace the global correlation for the computation of solid retention time. Since a number of key model variables and parameters are identified on-line using measurement data, the model is able to closely track the dynamic behaviour of rotary drying processes within a broad range of operational conditions. This adaptive model is validated against experimental data obtained from a pilot-scale rotary sugar dryer. The proposed modelling methodology can be easily incorporated into nonlinear model based control schemes to form a unified modelling and control framework.     

A DYNAMIC MODEL WITH ON-LINE IDENTIFICATION FOR ROTARY SUGAR DRYING PROCESSES

A dynamic modelling methodology, which combines on-line variable estimation and parameter identification with physical laws to form an adaptive model for rotary sugar drying processes, is developed in this paper. In contrast to the conventional rate-based models using empirical transfer coefficients, the heat and mass transfer rates are estimated by using on-line measurements in the new model. Furthermore, a set of improved sectional solid transport equations with localized parameters is developed in this work to replace the global correlation for the computation of solid retention time. Since a number of key model variables and parameters are identified on-line using measurement data, the model is able to closely track the dynamic behaviour of rotary drying processes within a broad range of operational conditions. This adaptive model is validated against experimental data obtained from a pilot-scale rotary sugar dryer. The proposed modelling methodology can be easily incorporated into nonlinear model based control schemes to form a unified modelling and control framework.     

3D morphology of Au and Au@Ag nanobipyramids

The morphologies of Au and Au@Ag nanobipyramids were investigated using electron tomography. The 3D reconstruction reveals that the Au bipyramids have an irregular six-fold twinning structure with highly stepped dominant {151} facets. These short steps/edges stabilized via surface adsorbed CTAB favor the growth of silver on the lateral facets leading to strong blue shifts in longitudinal plasmon surface resonance.     

MODEL PREDICTIVE CONTROL (MPC) AND ITS CURRENT ISSUES IN CHEMICAL ENGINEERING

Model predictive control (MPC) is one of the main process control techniques explored in the recent past; it is the amalgamation of different technologies used to predict future control action and future control trajectories knowing the current input and output variables and the future control signals. It can be said that the MPC scheme is based on the explicit use of a process model and process measurements to generate values for process input as a solution of an on-line (real-time) optimization problem to predict future process behavior. There have been a number of contributions in the field of nonlinear model–based predictive control dealing with issues like stability, efficient computation, optimization, constraints, and others. New developments in nonlinear MPC (NMPC) approaches come from resolving various issues, from faster optimization methods to different process models. This article specifically deals with chemical engineering systems ranging from reactors to distillation columns where MPC plays a role in the enhancement of the systems’ performance.     

Advance application of Raman spectroscopy for quantitative analysis of noncrystalline components in thin films of poly(ε-caprolactone)/poly(butadiene) blends

A quantitative analysis method for the distribution of noncrystalline poly(butadiene) component in poly(ε-caprolactone)/poly(butadiene) (PCL/PB) binary blends have been analyzed by advance application of Raman spectroscopy, optical microscopy, and differential scanning calorimetry (DSC) techniques. Thin films of different compositions of PCL/PB binary blends were prepared from solution and isothermally crystallized at a certain temperature. After calibration with real data, quantitative analyses by Raman spectroscopy revealed the amorphous PB are trapped inside the PCL crystals. Polarized optical microscopy and real time atomic force microscopy were used to collect data for the crystal morphology and crystal growth rate. For pure PCL crystals, a morphology of truncated lozenge shape was observed, independent of crystallization temperature and regardless of the blends compositions. For the pure PCL and their blends, almost unique crystal growth rate was found. The miscibility behaviors using DSC were drawn through melting point depression method. The Hoffman-Weeks extrapolations of the blends were found to be linear and identical with those of the neat PCL. The interaction parameter for the blends indicating that the PCL and PB blends have no intermolecular interaction, confirming the blends are immiscible. Despite the immiscibility of the blend, the PCL crystals do not bend during the growth process and do not reduce the growth rate as they do for miscible blend systems.     

On-line estimation of microbial specific growth-rates: An illustrative case study

This paper deals with the on-line estimation of the specific growth rate in a fermentation process in the case when a soluble product is measured on-line. The proposed algorithm belongs to a larger class of on-line estimation algorithms which present the following advantages: they do not require any analytical expressions for the specific growth rate; their theoretical stability and convergence have been emphasized; since their structure includes a filtering effect, the estimation results exhibit less erratic values for the specific growth rate than when obtained by direct calculation from the biomass concentration equation. Real-life results on an ethanol producing process are presented. Finally. it is shown how estimation results can be used for modelling purposes.     

Current methods for characterising mixing and flow in microchannels

This article reviews existing methods for the characterisation of mixing and flow in microchannels, micromixers and microreactors. In particular, it analyses the current experimental techniques and methods available for characterising mixing and the associated phenomena in single and multiphase flow. The review shows that the majority of the experimental techniques used for characterising mixing and two-phase flow in microchannels employ optical methods, which require optical access to the flow, or off-line measurements. Indeed visual measurements are very important for the fundamental understanding of the physics of these flows and the rapid advances in optical measurement techniques, like confocal scanning laser microscopy and high resolution stereo micro particle image velocimetry, are now making full field data retrieval possible. However, integration of microchannel devices in industrial processes will require on-line measurements for process control that do not necessarily rely on optical techniques. Developments are being made in the areas of non-intrusive sensors, magnetic resonance techniques, ultrasonic spectroscopy and on-line flow through measurement cells. The advances made in these areas will certainly be of increasing interest in the future as microchannels are more frequently employed in continuous flow equipment for industrial applications.     

(Tropical) soil organic matter modelling: problems and prospects

Soil organic matter plays an important role in many physical, chemical and biological processes. However, the quantitative relations between the mineral and organic components of the soil and the relations with the vegetation are poorly understood. In such situations, the use of models is an important research tool to explore the relations between the various components, to increase insight into processes, to examine the consequences of management, and to explore the possibilities for modification. An overview is given of the state-of-the-art in modelling of soil organic matter dynamics, with special emphasis on the processes in tropical regions. Major points identified as insufficiently developed include: Methodology is lacking to quantitatively describe the impact of soil texture and soil structure on the activity of soil biota. Effects of the microfauna on modelled organic matter transformations in the soil appear to be lacking. Techniques for direct measurement of pool sizes and characterisation of the relevant properties of the variety of organic substances would represent a major step towards verification of models and the revision of inherent concepts. The non-nutrient functions of organic matter, such as structural stability, water holding capacity and cation exchange capacity, need far more attention in modelling of soil organic matter. This revised version was published online in June 2006 with corrections to the Cover Date.     

Dynamic study and control of crystal size distribution (CSD) in a KCL crystallizer

Previous works on crystal size distribution (CSD) control have indicated that a successful control scheme requires on-line measurement of nuclei density. Such measurements can only be performed using an extremely expensive particle size analyzer. Besides the cost, the accuracy of such analyzers is restricted to low concentration slurries. In the present work an attempt is made to replace nuclei density by an alternative process variable which can be measured easily and inexpensively. Theoretical analysis of a KCl crystallizer shows that slurry density in the fines loop is an excellent substitute for nuclei density. Preliminary tests show that a turbidimeter can be used for measurement of slurry density in the fines loop. Proportional direct feedback control based on measurement of slurry concentration in the fines loop and manipulation of fines removal rate is shown to stabilize an inherently unstable crystallizer. The ability of the control scheme to suppress outside disturbances is also verified.