Search

Various types of risks exist in a supply chain, and disruptions could lead to economic loss or even breakdown of a supply chain without an effective mitigation strategy. The ability to detect disru...

This work addresses the problem of identifying models using process data with possibly correlated manipulated variables for model predictive control (MPC) design. The key idea is to use principal component analysis (PCA) to reject the redundancy in the input space and utilize scores to build the dynamic model of the system using recurrent neural networks (RNN). The identified PCA-RNN model is then used in the MPC optimization problem, calculating the optimal scores. The control actions are computed using the loadings of the PCA model. The efficacy of the proposed approach is evaluated using a chemical reactor example. The results are compared with a base-case scenario where the data is directly used to build a dynamic neural network model and used as part of a model predictive control implementation. The simulation results show the superiority of the proposed integrated PCA-RNN models for model predictive control.

The development of in situ-gelling hydrogels that can enable prolonged protein release is increasingly important due to the emergence of a growing number of protein-based therapeutics. Herein, we describe a high-throughput strategy to fabricate, characterize, and subsequently optimize hydrazone-cross-linked in situ-gelling hydrogels for protein delivery. Hydrogels are fabricated using an automated high-throughput robot to mix a variety of thermoresponsive, nonthermoresponsive, charged, neutral, naturally sourced, and synthetic polymers functionalized with hydrazide or aldehyde groups, generating in situ-gelling hydrogels with well-defined compositions within a 96-well plate. High-throughput characterization strategies are subsequently developed to enable on-plate analysis of hydrogel swelling, mechanics, degradation, transparency, and protein (ovalbumin) release kinetics that yield results consistent with those collected using traditional bulk hydrogel analysis techniques. Dynamic regression and latent variable modeling are then applied to fit performance statistics to the collected data set; subsequently, numerical optimization is used to identify mixtures of precursor polymers that exhibit targeted combinations of minimal burst release, maximum total protein release, minimum release rate, and maximum transparency (the latter of particular relevance for ophthalmic protein delivery applications). Given the rapid throughput of the protocols developed (i.e., 126 hydrogels can be synthesized and screened in quadruplicate within hours), this approach offers particular promise for accelerating the identification of injectable hydrogel compositions relevant for both protein delivery as well as other biomedical applications for which clearly predefined materials properties are required.

This manuscript addresses the problem of modeling an industrial (Sartorius) bioreactor using process data. In the context of the Sartorius Bioreactor, it is important to appropriately address the problem of dealing with a large number of variables, which are not always measured or are measured at different sampling rates, without taking recourse to simpler interpolation- or imputation-based approaches. To this end, a dynamic model for the Sartorius Bioreactor is developed via appropriately adapting a recently presented subspace model identification technique, which in turn uses nonlinear iterative partial least squares (NIPALS) algorithms to gracefully handle the missing data. The other key contribution is evaluating the ability of the identification approach to provide insight into the process by computing interpretable variables such as metabolite rates. The results demonstrate the ability of the proposed approach to model data from the Sartorius Bioreactor.

Covalently cross-linked and hydrolytically degradable poly(oligoethylene glycol methacrylate) (POEGMA)-based nanogels are fabricated using an all-aqueous self-assembly approach. The nanogels are composed of hydrazide- (POH) and aldehyde-functionalized (POA) POEGMA precursor polymers that exhibit lower critical solution temperature (LCST) behavior in aqueous media and form a covalent, yet degradable, hydrazone linkage upon mixing. By systematically changing the chemistry of the core and cross-linking precursor polymers, the concentration of the core precursor polymer, the ratio of core to cross-linking precursor polymer, and the temperature at which the assembly is conducted, a library of nanogels was produced with significant differences in size, polydispersity, and colloidal stability. Multivariate statistics indicates the presence of significant nonlinear responses within the process variables as well as correlations between the output variables, reflective of the complex balance of aggregation and stab...

This work addresses the problem of determining a data-driven model for the startup of a hydrogen production unit, and demonstrates the approach both on a detailed first principles simulation model and by application to real data. To this end, first a detailed first principles model of the hydrogen plant is developed in Honeywell's UniSim design by adapting the plant standard operating procedure (SOP). Illustrative simulations are next presented to establish the meaningfulness of approximating process nonlinearity with a (higher order) linear time invariant (LTI) model. Then an LTI data-driven model of the hydrogen unit startup process using subspace identification based methods is identified. The framework is then implemented and successfully validated data on simulated data and on data from an industrial hydrogen unit.

Batch process reactors are often used for products where quality is of paramount importance. To this end, this work addresses the problem of direct, data-driven, quality control for batch processes. Specifically, previous results using subspace identification for modeling dynamic evolution and making quality predictions are extended with two key novel contributions: first, a method is proposed to account for midbatch ingredient additions in both the modeling and control stages. Second, a novel model predictive control scheme is proposed that includes batch duration as a decision variable. The efficacy of the proposed modeling and control approaches are demonstrated using a simulation study of a poly(methyl methacrylate) (PMMA) reactor. Closed loop simulation results show that the proposed controller is able to reject disturbances in feed stock and drive the number-average molecular weight, weight-average molecular weight, and conversion to their respective set-points. Specifically, mean absolute percentag...

This paper addresses the problem of designing and implementing a data-driven model based model predictive controller (MPC). In particular, we consider the problem where a subspace identification approach is utilized to determine a state-space model, while applying first-principles based knowledge in the model identification (denoted as the constrained subspace model). The incorporation of the first-principles based constraints in the subspace matrix Patel et al. (2020) often leads to a feed-through matrix being present. Such a model then is the best representation of the system dynamics, but does not lend itself readily to existing linear MPC formulations where the feed-through matrix is assumed to be zero. Thus, an existing linear MPC formulation is adapted to handle the feed through matrix. The superior performance of this MPC design, which can utilize the constrained subspace model, over existing approaches is demonstrated using a two tank chemical stirred tank reactor process.

A PEGylated protein is prepared by conjugating polyethylene glycol (or PEG) with the protein, a process known as PEGylation. Most PEGylation processes lead to synthesis of different PEGylated forms of the protein, among which only one form is typically of interest. In this work, we propose a modeling and optimization-based approach for determining optimal operating conditions for protein PEGylation. To this end, a first-principles model is proposed and targeted experiments are carried out to estimate the model parameters. A simulation-based optimization is then carried out to suggest the best operating conditions. Specifically, results suggest that to maximize the concentration of mono-PEGylated product, the reaction should be carried out at high pH and with a high ratio of PEG to protein. Subsequent experiments are conducted to confirm the validity of the modeling and optimization approach.

In this Chapter, a novel data-driven, quality modeling and control approach for batch processes is presented. Specifically, subspace identification methods are adapted for use with batch data to identify a state-space model from available process measurements and input moves. The resulting LTI, dynamic, state-space model is shown to be able to describe the transient behavior of finite duration batch processes. Next, the terminal quality is related to the terminal value of the identified states. Finally, the resulting model is applied in a shrinking-horizon, model predictive control scheme to directly control terminal product quality. The theoretical properties of the proposed approach are studied and compared to state-of-the-art latent variable control approaches. The efficacy of the proposed approach is demonstrated through a simulation study of a batch polymethyl methacrylate (PMMA) polymerization reactor. Results for both disturbance rejection and set-point changes (that is, new quality grades) are demonstrated.

While injectable in situ cross-linking hydrogels have attracted increasing attention as minimally invasive tissue scaffolds and controlled delivery systems, their inherently disorganized and isotropic network structure limits their utility in engineering oriented biological tissues. Traditional methods to prepare anisotropic hydrogels are not easily translatable to injectable systems given the need for external equipment to direct anisotropic gel fabrication and/or the required use of temperatures or solvents incompatible with biological systems. Herein, we report a new class of injectable nanocomposite hydrogels based on hydrazone cross-linked poly(oligoethylene glycol methacrylate) and magnetically aligned cellulose nanocrystals (CNCs) capable of encapsulating skeletal muscle myoblasts and promoting their differentiation into highly oriented myotubes in situ. CNC alignment occurs on the same time scale as network gelation and remains fixed after the removal of the magnetic field, enabling concurrent CNC orientation and hydrogel injection. The aligned hydrogels show mechanical and swelling profiles that can be rationally modulated by the degree of CNC alignment and can direct myotube alignment both in two- and three-dimensions following coinjection of the myoblasts with the gel precursor components. As such, these hydrogels represent a critical advancement in anisotropic biomimetic scaffolds that can be generated noninvasively in vivo following simple injection.

In this work, the problem of determining a data-driven model of a hydrogen production unit is addressed. The framework is applied to a high fidelity simulation model developed in this work. To this end, first a high fidelity model of the entire plant is developed in Honeywell's UniSim Design, capable of simulating the startup and shutdown phase, with appropriate adaptation of the plant standard operating procedure (SOP). Several startups are simulated to generate training data for identification of a data-driven model. Then an LTI data-driven model of the process using subspace identification based methods is determined and validated against new simulated startup. Simulation results demonstrate the prediction capabilities of the identified model.

Batch process reactors are often used for products where quality is of paramount importance. To this end, this work addresses the problem of direct, data-driven, quality control for batch processes. Specifically, previous results using subspace identification for modeling dynamic evolution and making quality predictions are extended with two key novel contributions: first, a method is proposed to account for mid-batch ingredient additions in both the modeling and control stages. Second, a novel model predictive control scheme is proposed that includes batch duration as a decision variable. The efficacy of the proposed modeling and control approaches are demonstrated using a simulation study of a polymethyl methacrylate (PMMA) reactor. Closed loop simulation results show that the proposed controller is able to reject disturbances in feed stock and drive the number average molecular weight, weight average molecular weight, and conversion to their respective set-points. Specifically, mean absolute percentage errors (MAPE) in these variables are reduced from 8.66%, 7.87%, and 6.13% under traditional PI control to 1.61%, 1.90%, and 1.67% respectively.

Semicontinuous distillation is a process intensification technique for purification of multicomponent mixtures. The system is control-driven and thus the control structure and its tuning parameters have crucial importance in the operation and the economics of the process. In this study, for the first time, a model predictive control (MPC) formulation is implemented on a semicontinuous process to evaluate the associated closed-loop cost. A cascade configuration of MPC and PI controllers is designed in which the setpoints of the PI controllers are determined via a shrinking-horizon MPC. The objective is to reduce the operating cost of a cycle while simultaneously maintaining the required product qualities. A subspace identification method is adopted to identify a linear, state-space model to be used in the MPC. The first-principals model of the process is then simulated in gPROMS. Simulation results demonstrate that the MPC has reduced the operational cost of a semicontinuous process by about 11%. Ontario Trillium Scholarships

This is the Author Accepted Manuscript issued with: Creative Commons Attribution Non-Commercial License (CC:BY:NC 4.0)., This paper explains the development of a through-wall synthetic aperture radar (SAR) simulator, which is being used to investigate the SAR artefacts originating from vibrating target’s, known as paired echoes. The simulation and experimental results both show that paired echoes can be detected and imaged through a wall, with a noticeable reduction in intensity, resulting in the number of visible echoes to be reduced in brightness and appear shifted in location in a through-wall SAR image

The formation of Synthetic Aperture Radar (SAR) imagery requires many assumptions during data acquisition (e.g. assuming a flat focal plane, imaging in the far field, isotropic scattering). If a SAR image formation processor (IFP) does not account for these, then subtle, real-world effects such as those from a vibrating target or multipath signatures can become difficult to isolate from processor-induced artefacts. A SAR simulator has been developed that simulates the raw phase history that would be collected from a scene and input into a SAR IFP. This can then be passed through an operational SAR processor and the resulting image compared with the field-collected SAR image. This allows a precise observation of subtle features (such as those from a vibrating target) to be made. A super-fine resolution Ground-Based SAR system developed by Cranfield University was used to collect true multipath and vibrating signatures and a comparison made.

In this work we present a novel, data-driven, quality modeling and control approach for batch processes. Specifically, we adapt subspace identification methods for use with batch data to identify a state-space model from available process measurements and input moves. We demonstrate that the resulting LTI, dynamic, state-space model is able to describe the transient behavior of finite duration batch processes. Next, we relate the terminal quality to the terminal value of the identified states. Finally, we apply the resulting model in a shrinking-horizon, model predictive control scheme to directly control terminal product quality. The theoretical properties of the proposed approach are studied and compared to state-of-the-art latent variable control approaches. The efficacy of the proposed approach is demonstrated through a simulation study of a batch polymethyl methacrylate (PMMA) polymerization reactor. Results for both disturbance rejection and set-point changes (that is, new quality grades) are demonstrated.