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This work proposes a Generic Model Control (GMC) strategy to regulate biomass growth in fed-batch cultures of Escherichia coli BL21(DE3). The control law is established using a previously validated mechanistic model based on the overflow metabolism paradigm. A model reduction is carried out to prevent the controller from relying on kinetics, which may be uncertain. In order to limit the controller to the use of a single measurement, i.e., biomass concentration which is readily available, a Kalman filter is designed to reconstruct the nonmeasurable information from the outlet gas and the remaining stoichiometry. Several numerical simulations are presented to assess the controller robustness with respect to model uncertainty. Experimental validation of the proposed GMC strategy is achieved with a lab-scale bioreactor.

Distillation columns represent the most widely used separation equipment in the petrochemical industry. It is usually difficult to apply the traditional mechanism modeling method to online optimization and control because of its complex structure, and common simplified models produce obvious errors. Therefore, we analyze the mass transfer process of gas-liquid fluid on each column tray based on the theory of gas-liquid equilibrium and establish a nonlinear dynamic model of the distillation process. The proposed model can accurately characterize the nonlinear characteristics of the distillation process, and the model structure is largely simplified compared with the traditional mechanism model. Therefore, the model provides a new approach for model-based methods in distillation columns, especially for cases that require efficient online models. Two case studies of benzene-toluene distillation systems show that the nonlinear model has high concentration observation accuracy. Finally, a generic model control scheme is designed based on this model. Simulation results show that this control strategy performs better than a traditional PID control scheme.

A new and more appropriate continuous recycled system for aBiological Nutrient Removal process has been developed based on a sequencing batch reactor. This system comprises a Continuously Stirred Tank Reactor, a surge tank and a settling tank, from which a fraction of treated water is recycled back to the reactor. To design the control system for the whole plant, step tests have been conducted and Relative Gain Array analysis performed. Six control loops with the Process Variables including dissolved oxygen and nitrate concentrations, and volume holdups have been formed. Two designed control strategies Proportional Integral controllers and Generic Model Control have been implemented. The simulated results will be presented for comparison.

A model-based control algorithm (generic model control) is presented for fed-batch cultivation of recombinant Escherichia coli producing either transcriptional or translational fusion products. With the recent development of translational and operon fusions using green fluorescent protein (GFP) [Albano et al. (1998) Biotechnol Prog 14:351–354] along with an on-line GFP sensor [Randers-Eichhorn et al. (1997) Biotechnol Bioeng 55:921–926], real-time measurements of foreign protein level are now possible. A mathematical model is presented that is both accurate and simple so as to ensure that all state variables remain observable during cultivation. A balance between model accuracy and mathematical tractability was obtained to facilitate the formulation of the control algorithm. Generic model control (GMC) is a process model-based control algorithm incorporating a process model directly within the control structure. GMC was desirable since linearization of the process model was not necessary and robust performance could be obtained despite process disturbances or plant/model mismatch. Furthermore, a time-delay compensator was built into the control law to account for the observed 90-min lag associated with GFP fluorescence. The feasibility of the GMC algorithm was demonstrated by simulations.

Microalgae culture for CO 2 sequestration must be maintained at optimal operating conditions in order to maximize CO 2 biofixation. In the case of the microalgae culture, some recent studies have focused on the optimal control of cultures in a photobioreactor. These studies concern either pH regulation, or concentration regulation in the bioreactor. The chapter discusses three control laws to monitor the microalgae growth: generic model control (GMC) law, input/output linearizing control law, and Nonlinear Model Predictive Control (NMPC) law. Finally, their performances are illustrated in case of Chlorella vulgaris cultures, run in the lab‐scale photobioreactor. The performances of the control laws highlight the relevance of our bioprocess control strategy in regulating the cell concentration, to obtain high and stable levels of biomass productivity.

Faced with the global crisis of COVID-19 and the strong increase in customer demands, competition is becoming more intense between companies, on the one hand, and supply chains on the other. This competition has led to the development of new strategies to manage demand and increase market share. Among these strategies are the growing interest in sustainable manufacturing and the need for customizable products that create an increasingly complex manufacturing environment. Sustainable manufacturing and the need for customizable products create an environment of increased competition and constant change. Indeed, companies are trying to establish more flexible and agile manufacturing systems through several systems of reconfiguration. Reconfiguration contributes to an extension of the manufacturing system’s life cycle by modifying its physical, organizational and IT characteristics according to the changing market conditions. Due to the rapid development of new information technology (such as IoT, Big Data analytics, cyber-physical systems, cloud computing and artificial intelligence), digital twins have become intensively used in smart manufacturing. This paper proposes a digital twin design and simulation model for reconfigurable manufacturing systems (RMSs).

Transformable Unmanned Aerial Systems (UASs) are increasingly attracting attention in recent years due to their maneuverability, agility and morphological capacities. They have overcame many limitations such as, multi-tasks problem, structural adaptation in flight, energy consumption, fault tolerant control, and maneuverability. Nevertheless, their variable geometries as well as the great number of actuators make them highly nonlinear and over-actuated systems, which are characterized by a slow transformation mechanism, variable mathematical models, and complex design and control architectures. In this article, we propose a simple and lightweight design of a transformable quadrotor, which allows to increase the geometric adaptability in flight, maneuverability, and speed of the transformation process by exploiting fast and performant servomotors. Since the Center of Gravity (CoG) of the quadrotor varies according to the desired shape, it results in a variation of the inertia and the control matrix instantly. These parameters play a crucial role in the system control and its stability, which is substantially a key difference compared to the classic quadrotor. Thus, a new generic model will be developed, which takes into account all these variations together and the asymmetry of the configurations. To validate the developed model, ensure the stability of our quadrotor, and improve the performance of the linear control strategies applied to these new drones, an optimal gain scheduling backstepping controller based on Particle Swarm Optimization (PSO) algorithm will be designed and tested. The realized prototype will be presented at the end of this work.

We develop a structural model to investigate the effects of pharmaceutical price regulation on demand and on manufacturers’ price-setting behavior in France. We estimate price-cost margins in a regulated market with price constraints and infer whether these constraints are binding, exploiting cost restrictions across drugs, which come from observing the same drugs in potentially price-constrained markets (France) and in markets where prices are unregulated (United States and Germany). Our counterfactual simulations suggest that price constraints generated modest savings for anti-ulcer drugs in 2003–2013 (2 percent of total expenses), relative to a free pricing scenario, and shifted consumption from generic to branded drugs.

Chemometric models for on-line process monitoring have become well established in pharmaceutical bioprocesses. The main drawback is the required calibration effort and the inflexibility regarding system or process changes. So, a recalibration is necessary whenever the process or the setup changes even slightly. With a large and diverse Raman dataset, however, it was possible to generate generic partial least squares regression models to reliably predict the concentrations of important metabolic compounds, such as glucose-, lactate-, and glutamine-indifferent CHO cell cultivations. The data for calibration were collected from various cell cultures from different sites in different companies using different Raman spectrophotometers. In testing, the developed “generic” models were capable of predicting the concentrations of said compounds from a dilution series in FMX-8 mod medium, as well as from an independent CHO cell culture. These spectra were taken with a completely different setup and with different Raman spectrometers, demonstrating the model flexibility. The prediction errors for the tests were mostly in an acceptable range (<10% relative error). This demonstrates that, under the right circumstances and by choosing the calibration data carefully, it is possible to create generic and reliable chemometric models that are transferrable from one process to another without recalibration.

Background In a context where the economic burden of HIV is increasing as HIV patients now have a close to normal lifespan, the availability of generic antiretrovirals commonly prescribed in 2017 and the imminence of patent expiration are expected to provide substantial savings in the coming years. This article aims to assess the economic impact of these generic antiretrovirals in France and specifically over a five-year period. Methods An agent-based model was developed to simulate patient trajectories and treatment use over a five-year period. By comparing the results of costs for trajectories simulated under different predefined scenarios, a budget impact model can be created and sensitivity analyses performed on several parameters of importance. Results The potential economic savings from 2019 to 2023 generated by generic antiretrovirals range from €309 million when the penetration rate of generics is set at 10% to €1.5 billion at 70%. These savings range from €984 million to €993 million as the delay between patent and generic marketing authorisation varies from 10 to 15 years, and from €965 million to €993 million as the Negotiated Price per Unit (NPU) of generics at market-entry varies from 40 to 50% of the NPU for patents. Discussion This economic savings simulation could help decision makers to anticipate resource allocations for further innovation in antiretrovirals therapies as well as prevention, especially by funding the Pre-Exposure Prophylaxis (PrEP) or HIV screening.