Academic literature on the topic 'Emulsion, terpolymerisation, modelling, control'

This paper presents some design aspects of the EMULSION IoT platform, developed as a typical example of the horizontal IoT platforms. The architectural overview and multi-tiered structure of the platform are described, with special attention being paid to its modelling & simulation tier as a novel architectural element proposed for inclusion in similar IoT platforms. Used to model cyber-physical-social (CPS) objects and IoT services, along with their attributes and temporal/spatial/event characteristics, this tier is also utilized to simulate the actual provision of IoT services in order to determine the optimal configuration of the platform in each particular use case, by solving complex optimization tasks. Examples of such tasks are presented in the paper along with some results obtained to date.

The strong need to control investments related to oil extraction and the growing demand for offshore deep-water exploration are the reasons for looking for tools to make up a global underwater monitoring system. Therefore, the current study analyses the possibility of revealing the existence of oil-in-water emulsions in the water column, based on the modelling of the downwelling radiance detected by a virtual underwater sensor. Based on the Monte Carlo simulation for the large numbers of solar photons in the water, the analyses were carried out for eight wavelengths ranging from 412 to 676 nm using dispersed oil with a concentration of 10 ppm. The optical properties of the seawater were defined as typical for the southern Baltic Sea, while the oil emulsion model was based on the optical properties of crude oil extracted in this area. Based on the above-mentioned assumptions and modelling, a spectral index was obtained, with the most favourable combination of 555/412 nm, whose value is indicative of the presence of an oil emulsion in the water.

Summary In this paper, we describe a laboratory investigation of a nonionic surfactant for carbon dioxide-(CO2-) foam mobility control in the East Seminole field, a heterogeneous carbonate reservoir in the Permian Basin of west Texas. A method of high-performance liquid chromatography-evaporativelight-scattering detector (HPLC-ELSD) was followed for characterizing the surfactant stability. The foam transport process was studied in the absence and the presence of East Seminole crude oil, with test results showing that strong CO2-foam forms in either a bulk-foam test or foam-flow test. An oxygen scavenger, carbohydrazide, was found effective for controlling the stability of the surfactant up to 80°C and total dissolved solid of ∼34,000 ppm. Moreover, a phosphonate scale inhibitor was investigated and found to be compatible with the oxygen scavenger to accommodate a surfactant solution in a gypsum-oversaturated reservoir brine. During the oil-fractional flow test, an emulsion appears to form, causing a noticeable pressure increase; however, emulsion generation failed to cause a significant phase plugging in the test. Also, a STARS™ (Computer Modelling Group Ltd., Calgary, Alberta, Canada) foam model was applied to obtain the foam parameters from the foam-flow experiments at steady-state conditions. The insights from laboratory experiments better enable translation of the foam technology to the field.

The rheology of highly concentrated monodisperse emulsions is studied by rigorous multidrop numerical simulations for three types of steady macroscopic flow, (i) simple shear ($\dot{{\it\gamma}}x_{2}$, 0 0), (ii) planar extension (PE) ($\dot{{\it\Gamma}}x_{1},-\dot{{\it\Gamma}}x_{2},0$) and (iii) mixed ($\dot{{\it\gamma}}x_{2}$, $\dot{{\it\gamma}}{\it\chi}x_{1}$, 0), where $\dot{{\it\gamma}}$ and $\dot{{\it\Gamma}}$ are the deformation rates, and ${\it\chi}\in (-1,1)$ is the flow parameter, in order to construct and validate a general constitutive model for emulsion flows with arbitrary kinematics. The algorithm is a development of the multipole-accelerated boundary-integral (BI) code of Zinchenko & Davis (J. Fluid Mech., vol. 455, 2002, pp. 21–62). It additionally incorporates periodic boundary conditions for (ii) and (iii) (based on the reproducible lattice dynamics of Kraynik–Reinelt for PE), control of surface overlapping, much more robust controllable surface triangulations for long-time simulations, and more efficient acceleration. The emulsion steady-state viscometric functions (shear viscosity and normal stress differences) for (i) and extensiometric functions (extensional viscosity and stress cross-difference) for (ii) are studied in the range of drop volume fractions $c=0.45{-}0.55$, drop-to-medium viscosity ratios ${\it\lambda}=0.25{-}10$ and various capillary numbers $\mathit{Ca}$, with 100–400 drops in a periodic cell and 2000–4000 boundary elements per drop. High surface resolution is important for all three flows at small $\mathit{Ca}$. Large system size and strains $\dot{{\it\gamma}}t$ of up to several thousand are imperative in some shear-flow simulations to identify the onset of phase transition to a partially ordered state, and evaluate (although still not precisely) the viscometric functions in this state. Below the phase transition point, the shear viscosity versus $\mathit{Ca}$ shows a kinked behaviour, with the local minimum most pronounced at ${\it\lambda}=1$ and $c=0.55$. The ${\it\lambda}=0.25$ emulsions flow in a partially ordered manner in a wide range of $\mathit{Ca}$ even when $c=0.45$. Increase of ${\it\lambda}$ to 3–10 shifts the onset of ordering to much smaller $\mathit{Ca}$, often outside the simulation range. In contrast to simple shear, phase transition is never observed in PE or mixed flow. A generalized five-parameter Oldroyd model with variable coefficients is fitted to our extensiometric and viscometric functions at arbitrary flow intensities (but outside the phase transition range). The model predictions compare very well with precise simulation results for strong mixed flows, ${\it\chi}=0.25$. Time-dependent PE flow is also considered. Ways to overcome the phase transition and drop breakup limitations on constitutive modelling are discussed.

Accurate control of product properties through the manipulation of transfer agents can be of great benefit to industry in producing targeted polymeric materials. In this work we developed experimental protocols and mathematical models for understanding and characterising semi-batch emulsion polymerization in the presence of a xanthate-based transfer agent. Zero-one kinetics was employed with population balance equations to predict monomer conversion, molecular weight (MWD) and particle size (PSD) distributions in the presence of xanthate-based reversible addition-fragmentation chain transfer (RAFT) agents. The effects of the transfer agent (AR), surfactant, initiator (KPS) and temperature were investigated. Monomer feed rate was found to strongly affect conversion, MWD and PSD. The polymerization rate (Rp), number average molecular weight (Mn) and particle size () decreased with increasing AR. Rp increased with increase in SDS and KPS; while with increase in temperature, Mn decreased, Rp increased and increased. With semi-batch mode, Mn and increased with monomer flow rate.

Polymer manufacturing is a major worldwide industry, attracting the attention of numerous industrial units and research institutes. Increasing demands on polymer quality, process safety and cost reduction are the main reasons for growing interest in the design and control of emulsion polymerisation. Emulsion polymerisation process implemented with free radical polymerisation has significant advantages over other processes, such as the production of polymer of higher molecular weights at high conversion rates, easier temperature control due to the low viscosity of the reaction media, high degree of selectivity and more friendly to environment due to the use of an aqueous medium. It allows for the production of particles with specially-tailored properties, including size, composition, morphology, and molecular weights. Introducing two or more different monomers to the polymerisation process (named multi-polymerisation) can lead to the synthesis of an almost unlimited number of new polymers types. Emulsion polymers are products by process, meaning that the manner in which the polymerisation is carried out is perhaps more important than the raw materials in determining the form of the final product. This highlights the significance of the systematic approach in online process control which requires thorough understanding of the process phenomena as a prerequisite for development of a mathematical description of the process as the model. It is thus evident and based on research observations that process control for emulsion terpolymerisation is a particularly difficult task because of the lack of validated models and the lack of online measurements of most of polymer properties of interest. Therefore, a well validated model is crucial for optimising and controlling the emulsion terpolymerisation operations allowing for design of the polymer product properties. In this study, a framework for process design and control of emulsion terpolymerisation reactors was developed. This framework consisted of three consecutive stages, dynamic modelling of the process, optimising the process for finding the optimal operating strategies and final online controlling the obtained optimal trajectories through multivariable constrained model predictive control. Within this framework, a comprehensive dynamic model was developed. Then a test case of emulsion terpolymerisation of styrene, methyl methacrylate and methyl acrylate was investigated on state of the art facilities for predicting, optimising and control end-use product properties including global and individual conversions, terpolymer composition, the average particle diameter and concentration, glass transition temperature, molecular weight distribution, the number- and weight-average molecular weights and particle size distribution. The resulting model was then exploited to understand emulsion terpolymerisation behavior and to undertake model-based optimization to readily develop optimal feeding recipes. The model equations include diffusion-controlled kinetics at high monomer conversions, where transition from a ‘zero-one’ to a ‘pseudo-bulk’ regime occurs. Transport equations are used to describe the system transients for batch and semi-batch processes. The particle evolution is described by population balance equations which comprise of a set of integro-partial differential and nonlinear algebraic equations. Backward finite difference approximation method is used to discretise the population equation and convert them from partial differential equations to ordinary differential equations. The model equations were solved using the advanced simulation environment of the gPROMS package. The dynamic model was then used to determine optimal control policies for emulsion terpolymerisation in a semi-batch reactor using the multiobjective dynamic optimisation method. The approach used allows the implementation of constrained optimisation procedures for systems described by complex mathematical models describing the operation of emulsion terpolymerisation reactors. The control vector parameterisation approach was adopted in this work. Styrene monomer feed rate, MMA monomer feed rate, MA monomer feed rate, surfactant feed rate, initiator feed rate and the temperature of reactor were used as the manipulating variables to produce terpolymers of desired composition, molecular weight distribution (MWD) and particle size distribution (PSD). The particle size polydispersity index (PSPI), molecular weight polydispersity index (MWPI) and the overall terpolymer composition ratios were incorporated as the objective functions to optimise the PSD, MWD and terpolymer composition, respectively. The optimised operational policies were successively validated with experiments via one stirred tank polymerisation reactor. Due to the lack of online measurements of key process product attributes for emulsion terpolymerisation, an inferential calorimetric soft sensor was developed based on temperature measurements. The calorimetric soft sensor obtains online measurements of reactor temperature, jacket inlet and outlet temperatures helped estimate the rate of polymerisation. The model includes the mass and energy balance equations over the reactor and its peripherals. Energy balance equations include the heat of reaction, internal and external heat transfer effects, as well as external heat losses. An online multivariable constrained model predictive control was formulated and implemented for online control of the emulsion terpolymerisation process. To achieve this implementation, a novel generic multilayer control architecture for real-time implementation of optimal control policies for particulate processes was developed. This strategy implements the dynamic model for the emulsion terpolymerisation as a real-time soft sensor which is incorporated within the implemented MPC. The methodology was successively validated using six case studies within the on-line control of terpolymerisation reactors. The cases were online controlled the composition of terpolymers, PSD and Mn with specific constraints for the operation conversion and particle average radius. An advanced Supervisory Control Architecture named ROBAS was used in this work. It provides a completely automated architecture allowing for the real time advanced supervisory monitoring and control of complex systems. The real time control application strategy was developed within MATLAB, Simulink, gPROMS and Excel Microsoft softwares and implemented on line through ROBAS Architecture. The manipulated variables are measured using on-line measurements connected to the DCS system through Honeywell. These measurements were sent to MATLAB and then to the dynamic model in gPROMS through an excel spread-sheet interface. Then the dynamic model used them to estimate the controlled variables of the MPC. The estimated values of the controlled variables obtained from the dynamic model, were then sent to the Simulink and fed through the DCS system to the MPC developed in MATLAB. The MPC would then calculate optimal trajectories, which are then sent as set point signals through the DCS system to the regulatory controller. The MPC formulation was found to be robust and handles disturbances to the process. The result showed that the online multivariable constrained MPC controller was able to control the desired composition and Mn as specified set points with great accuracy. The MPC algorithm succeeded under constrained conditions, in driving the PSD to the desired target. Although some offset was observed with a certain degree of model mismatch, the experimental results agreed well with predictions.

Doctor of Philosophy(PhD)
Polymer manufacturing is a major worldwide industry, attracting the attention of numerous industrial units and research institutes. Increasing demands on polymer quality, process safety and cost reduction are the main reasons for growing interest in the design and control of emulsion polymerisation. Emulsion polymerisation process implemented with free radical polymerisation has significant advantages over other processes, such as the production of polymer of higher molecular weights at high conversion rates, easier temperature control due to the low viscosity of the reaction media, high degree of selectivity and more friendly to environment due to the use of an aqueous medium. It allows for the production of particles with specially-tailored properties, including size, composition, morphology, and molecular weights. Introducing two or more different monomers to the polymerisation process (named multi-polymerisation) can lead to the synthesis of an almost unlimited number of new polymers types. Emulsion polymers are products by process, meaning that the manner in which the polymerisation is carried out is perhaps more important than the raw materials in determining the form of the final product. This highlights the significance of the systematic approach in online process control which requires thorough understanding of the process phenomena as a prerequisite for development of a mathematical description of the process as the model. It is thus evident and based on research observations that process control for emulsion terpolymerisation is a particularly difficult task because of the lack of validated models and the lack of online measurements of most of polymer properties of interest. Therefore, a well validated model is crucial for optimising and controlling the emulsion terpolymerisation operations allowing for design of the polymer product properties. In this study, a framework for process design and control of emulsion terpolymerisation reactors was developed. This framework consisted of three consecutive stages, dynamic modelling of the process, optimising the process for finding the optimal operating strategies and final online controlling the obtained optimal trajectories through multivariable constrained model predictive control. Within this framework, a comprehensive dynamic model was developed. Then a test case of emulsion terpolymerisation of styrene, methyl methacrylate and methyl acrylate was investigated on state of the art facilities for predicting, optimising and control end-use product properties including global and individual conversions, terpolymer composition, the average particle diameter and concentration, glass transition temperature, molecular weight distribution, the number- and weight-average molecular weights and particle size distribution. The resulting model was then exploited to understand emulsion terpolymerisation behavior and to undertake model-based optimization to readily develop optimal feeding recipes. The model equations include diffusion-controlled kinetics at high monomer conversions, where transition from a ‘zero-one’ to a ‘pseudo-bulk’ regime occurs. Transport equations are used to describe the system transients for batch and semi-batch processes. The particle evolution is described by population balance equations which comprise of a set of integro-partial differential and nonlinear algebraic equations. Backward finite difference approximation method is used to discretise the population equation and convert them from partial differential equations to ordinary differential equations. The model equations were solved using the advanced simulation environment of the gPROMS package. The dynamic model was then used to determine optimal control policies for emulsion terpolymerisation in a semi-batch reactor using the multiobjective dynamic optimisation method. The approach used allows the implementation of constrained optimisation procedures for systems described by complex mathematical models describing the operation of emulsion terpolymerisation reactors. The control vector parameterisation approach was adopted in this work. Styrene monomer feed rate, MMA monomer feed rate, MA monomer feed rate, surfactant feed rate, initiator feed rate and the temperature of reactor were used as the manipulating variables to produce terpolymers of desired composition, molecular weight distribution (MWD) and particle size distribution (PSD). The particle size polydispersity index (PSPI), molecular weight polydispersity index (MWPI) and the overall terpolymer composition ratios were incorporated as the objective functions to optimise the PSD, MWD and terpolymer composition, respectively. The optimised operational policies were successively validated with experiments via one stirred tank polymerisation reactor. Due to the lack of online measurements of key process product attributes for emulsion terpolymerisation, an inferential calorimetric soft sensor was developed based on temperature measurements. The calorimetric soft sensor obtains online measurements of reactor temperature, jacket inlet and outlet temperatures helped estimate the rate of polymerisation. The model includes the mass and energy balance equations over the reactor and its peripherals. Energy balance equations include the heat of reaction, internal and external heat transfer effects, as well as external heat losses. An online multivariable constrained model predictive control was formulated and implemented for online control of the emulsion terpolymerisation process. To achieve this implementation, a novel generic multilayer control architecture for real-time implementation of optimal control policies for particulate processes was developed. This strategy implements the dynamic model for the emulsion terpolymerisation as a real-time soft sensor which is incorporated within the implemented MPC. The methodology was successively validated using six case studies within the on-line control of terpolymerisation reactors. The cases were online controlled the composition of terpolymers, PSD and Mn with specific constraints for the operation conversion and particle average radius. An advanced Supervisory Control Architecture named ROBAS was used in this work. It provides a completely automated architecture allowing for the real time advanced supervisory monitoring and control of complex systems. The real time control application strategy was developed within MATLAB, Simulink, gPROMS and Excel Microsoft softwares and implemented on line through ROBAS Architecture. The manipulated variables are measured using on-line measurements connected to the DCS system through Honeywell. These measurements were sent to MATLAB and then to the dynamic model in gPROMS through an excel spread-sheet interface. Then the dynamic model used them to estimate the controlled variables of the MPC. The estimated values of the controlled variables obtained from the dynamic model, were then sent to the Simulink and fed through the DCS system to the MPC developed in MATLAB. The MPC would then calculate optimal trajectories, which are then sent as set point signals through the DCS system to the regulatory controller. The MPC formulation was found to be robust and handles disturbances to the process. The result showed that the online multivariable constrained MPC controller was able to control the desired composition and Mn as specified set points with great accuracy. The MPC algorithm succeeded under constrained conditions, in driving the PSD to the desired target. Although some offset was observed with a certain degree of model mismatch, the experimental results agreed well with predictions.

Dynamic modelling and optimization of three different processes namely (a) bulk polymerization of styrene, (b) solution polymerization of methyl methacrylate (MMA) and (c) emulsion copolymerization of Styrene and MMA in batch and semi-batch reactors are the focus of this work. In this work, models are presented as sets of differential-algebraic equations describing the process. Different optimization problems such as (a) maximum conversion (Xn), (b) maximum number average molecular weight (Mn) and (c) minimum time to achieve the desired polymer molecular properties (defined as pre-specified values of monomer conversion and number average molecular weight) are formulated. Reactor temperature, jacket temperature, initial initiator concentration, monomer feed rate, initiator feed rate and surfactant feed rate are used as optimization variables in the optimization formulations. The dynamic optimization problems were converted into nonlinear programming problem using the CVP techniques which were solved using efficient SQP (Successive Quadratic Programming) method available within the gPROMS (general PROcess Modelling System) software. The process model used for bulk polystyrene polymerization in batch reactors, using 2, 2 azobisisobutyronitrile catalyst (AIBN) as initiator was improved by including the gel and glass effects. The results obtained from this work when compared with the previous study by other researcher which disregarded the gel and glass effect in their study which show that the batch time operation are significantly reduced while the amount of the initial initiator concentration required increases. Also, the termination rate constant decreases as the concentration of the mixture increases, resulting rapid monomer conversion. The process model used for solution polymerization of methyl methacrylate (MMA) in batch reactors, using AIBN as the initiator and Toluene as the solvent was improved by including the free volume theory to calculate the initiator efficiency, f. The effects of different f was examined and compared with previous work which used a constant value of f 0.53. The results of these studies show that initiator efficiency, f is not constant but decreases with the increase of monomer conversion along the process. The determination of optimal control trajectories for emulsion copolymerization of Styrene and MMA with the objective of maximizing the number average molecular weight (Mn) and overall conversion (Xn) were carried out in batch and semi-batch reactors. The initiator used in this work is Persulfate K2S2O8 and the surfactant is Sodium Dodecyl Sulfate (SDS). Reduction of the pre-batch time increases the Mn but decreases the conversion (Xn). The sooner the addition of monomer into the reactor, the earlier the growth of the polymer chain leading to higher Mn. Besides that, Mn also can be increased by decreasing the initial initiator concentration (Ci0). Less oligomeric radicals will be produced with low Ci0, leading to reduced polymerization loci thus lowering the overall conversion. On the other hand, increases of reaction temperature (Tr) will decrease the Mn since transfer coefficient is increased at higher Tr leading to increase of the monomeric radicals resulting in an increase in termination reaction.