Transfer learning for batch process optimal control using LV-PTM and adaptive control strategy

In this study, we investigate a data-driven optimal control for a new batch process. Existing data-driven optimal control methods often ignore an important problem, namely, because of the short operation time of the new batch process, the modeling data in the initial stage can be insufficient. To address this issue, we introduce the idea of transfer learning, i.e., a latent variable process transfer model (LV-PTM) is adopted to transfer sufficient data and process information from similar processes to a new one to assist its modeling and quality optimization control. However, due to fluctuations in raw materials, equipment, etc., differences between similar batch processes are always inevitable, which lead to the serious and complicated mismatch of the necessary condition of optimality (NCO) between the new batch process and the LV-PTM-based optimization problem. In this work, we propose an LV-PTM-based batch-to-batch adaptive optimal control strategy, which consists of three stages, to ensure the best optimization performance during the whole operation lifetime of the new batch process. This adaptive control strategy includes model updating, data removal, and modifier-adaptation methodology using final quality measurements in response. Finally, the feasibility of the proposed method is demonstrated by simulations

Optimal Feeding Trajectories Design for E. coli Fed-batch Fermentations

In this paper optimal control algorithms for two E. coli fed-batch fermentations are developed. Fed-batch fermentation processes of E. coli strain MC4110 and E. coli strain BL21(DE3)pPhyt109 are considered. Simple material balance models are used to describe the E. coli fermentation processes. The optimal feed rate control of a primary metabolite process is studied and a biomass production is used as an example. The optimization of the considered fed-batch fermentation processes is done using the calculus of variations to determine the optimal feed rate profiles. The problem is formulated as a free final time problem where the control objective is to maximize biomass at the end of the process. The obtained optimal feed rate profiles consist of sequences of maximum and minimum feed rates. The resulting profiles are used for optimization of the E. coli fed-batch fermentations. Presented simulations show a good efficiency of the developed optimal feed rate profiles

Application of flexible recipes for model building, batch process optimization and control

Unlike the traditionally fixed recipes in batch process operation, flexible recipes allow the adjustment of some of its relevant recipe items. These adjustments can either be predefined in cases of planned experimentation, or suggested by a formal process optimization or control algorithm on the basis of actual information. In both the response surface methodology and the simplex evolutionary operation (EVOP), some well-known methods for empirical model building and process optimization, flexible recipes are involved. Another application of flexible recipes arises in a feedforward quality control strategy of batch processes when variations in market or process conditions are known a priori. The experimental results of these strategies are presented for the batchwise production of benzylalcohol on a pilotplant scale. Experiments have been performed to obtain a reliable model of the yield. On the basis of this model, better process conditions have been suggested, which substantially deviate from the final simplex resulted from experiments within simplex EVOP. Finally, an adaptive feedforward control strategy has been applied for a priori known disturbances in the process inputs

Control and Optimization of Batch Chemical Processes

A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent “time” variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand, and repeated numerical optimization and optimizing control on the other. Several case studies are presented to illustrate the various approaches

OptFerm - a computational platform for the optimization of fermentation processes

We present OptFerm, a computational platform for the simulation and optimization of fermentation processes. The aim of this project is to offer a platform-independent, user-friendly, open-source and extensible environment for Bioengineering process optimization that can be used to increase productivity. This tool is focused in optimizing a feeding trajectory to be fed into a fed-batch bioreactor and to calculate the best concentration of nutrients to initiate the fermentation. Also, a module for the estimation of kinetic and yield parameters has been developed, allowing the use of experimental data obtained from batch or fed-batch fermentations to reach the best possible model setup. The software was built using a component-based modular development methodology, using Java as the programming language. AlBench. a Model-View-Control based application framework was used as the basis to implement the different data objects and operations, as well as their graphical user interfaces. Also, this allows the tool to be easily extended with new modules, currently being developed

The Development of Hybrid Process Control Systems For Fluidized Bed Pellet Coating Processes

The conventional basic control for pharmaceutical batch processes has several drawbacks. The basic control often uses constant process settings discovered by trial and error. The rigid process operation provides limited process understanding and forgoes the opportunities of process optimization. Product quality attributes are measured by the low efficient off-line tests, therefore these cannot be used to monitor and inform the process to make appropriate adjustments. Frequent reprocessing and batch failures are possible consequences if the process is not under effective control. These issues raise serious concerns of the process capability of a pharmaceutical manufacturing process. An alternative process control strategy is perceived as a logical way to improve the process capability. To demonstrate the strategy, a hybrid control system is proposed in this work. A challenging aqueous drug layering process, which had a batch failure rate of 30% when operated using basic control, was investigated as a model system to develop and demonstrate the hybrid control system. The hybrid control consisted of process manipulation, monitoring and optimization. First principle control was developed to manipulate the process. It used a theory of environmental equivalency to regulate a consistent drying rate for the drug layering process. The process manipulation method successfully eliminated the batch failures previously encountered in the basic control approach. Process monitoring was achieved by building an empirical analytical model using in-line Near-Infrared spectroscopy. The model allowed real time quantitative analysis of drug layered content and was able to determine the endpoint of the process. It achieved quality assurance without relying on the end product tests. Process optimization was accomplished by discovering optimum process settings in an operation space. The operation space was constructed using edge of failure analysis on a design space. It provided setpoints with higher confidence to meet the specifications. The integration of the control elements enabled a complete hybrid control system. The results showed the process capability of the drug layering process was significantly improved by using the hybrid control. The effectiveness was substantiated by statistical evidence of the process capability indices

A new representation in evolutionary algorithms for the optimization of bioprocesses

Evolutionary Algorithms (EAs) have been used to achieve optimal feedforward control in a number of fed-batch fermentation processes. Typically, the optimization purpose is to set the optimal feeding trajectory, being the feeding profile over time given by a piecewise linear function, in order to reduce the number of parameters to the optimization algorithm. In this work, a novel representation scheme for the encoding of the feeding trajectory over time is proposed. Each gene in the variable sized chromosome has two components: a time label and the real value of the variable. The new approach is compared with a traditional real-valued EA, with chromosomes of constant size and fixed discretization steps. Three distinct case studies are presented, taken from previous work from the authors and literature, all considering the optimization of fed-batch fermentation processes. The experimental results show that the proposed approach is capable of results better or at the same level of quality of the best traditional EAs and is able to automatically evolve the best discretization steps for each case, thus simplifying the EA's setup.Fundação para a Ciência e Tecnologia (FCT) - 59899/EIA/POSC/2004

Modelling, Optimization and Optimal Control of Small Scale Stirred Tank Bioreactors

Models of the mass-transfer in a stirred tank bioreactor depending on general indexes of the processes of aeration and mixing in concrete simplifications of the hydrodynamic structure of the flows are developed. The offered combined model after parameters identification is used for optimization of the parameters of the apparatus construction. The optimization problem is solved by using of the fuzzy sets theory and in this way the unspecified as a result of the model simplification are read. In conclusion an optimal control of a fed-batch fermentation process of E. coli is completed by using Neuro-Dynamic programming. The received results after optimization show a considerable improvement of the mass-transfer indexes and the quantity indexes at the end of the process