On-line yeast propagation process monitoring and control using an intelligent automatic control system

Challenges in the Development of Soft Sensors for Bioprocesses: A Critical Review

Among the greatest challenges in soft sensor development for bioprocesses are variable process lengths, multiple process phases, and erroneous model inputs due to sensor faults. This review article describes these three challenges and critically discusses the corresponding solution approaches from a data scientist’s perspective. This main part of the article is preceded by an overview of the status quo in the development and application of soft sensors. The scope of this article is mainly the upstream part of bioprocesses, although the solution approaches are in most cases also applicable to the downstream part. Variable process lengths are accounted for by data synchronization techniques such as indicator variables, curve registration, and dynamic time warping. Multiple process phases are partitioned by trajectory or correlation-based phase detection, enabling phase-adaptive modeling. Sensor faults are detected by symptom signals, pattern recognition, or by changing contributions of the corresponding sensor to a process model. According to the current state of the literature, tolerance to sensor faults remains the greatest challenge in soft sensor development, especially in the presence of variable process lengths and multiple process phases.

Fermentation of Saccharomyces cerevisiae - Combining kinetic modeling and optimization techniques points out avenues to effective process design

A combined experimental/theoretical approach is presented, for improving the predictability of Saccharomyces cerevisiae fermentations. In particular, a mathematical model was developed explicitly taking into account the main mechanisms of the fermentation process, allowing for continuous computation of key process variables, including the biomass concentration and the respiratory quotient (RQ). For model calibration and experimental validation, batch and fed-batch fermentations were carried out. Comparison of the model-predicted biomass concentrations and RQ developments with the corresponding experimentally recorded values shows a remarkably good agreement for both batch and fed-batch processes, confirming the adequacy of the model. Furthermore, sensitivity studies were performed, in order to identify model parameters whose variations have significant effects on the model predictions: our model responds with significant sensitivity to the variations of only six parameters. These studies provide a valuable basis for model reduction, as also demonstrated in this paper. Finally, optimization-based parametric studies demonstrate how our model can be utilized for improving the efficiency of Saccharomyces cerevisiae fermentations.

Anti-inflammatory therapies in TRAMP mice: delay in PCa progression

The aim of this study was to characterize the structural and molecular biology as well as evaluate the immediate and late responses of prostatic cancer in the transgenic adenocarcinoma of the mouse prostate (TRAMP) model after treatment with goniothalamin (GTN) and celecoxib. The treated mice received GTN (150 mg/kg, gavage) or celecoxib (10 mg/kg, gavage) from 8 to 12 weeks of age. They were killed at different ages: the immediate-response groups at 12 weeks and the late-response groups at 22 weeks. The ventral prostate was collected for light microscopy, immunohistochemistry, western blotting, TUNEL, and ELISA. Morphological analyses indicated that GTN treatment delayed the progression of prostatic adenocarcinoma, leading to a significant decrease of prostatic lesion frequency in both experimental period responses to this treatment, mainly high-grade prostatic intraepithelial neoplasia and well-differentiated adenocarcinoma. Also, the celecoxib treatment showed a particular decrease in the proliferative processes (PCNA) in both the experimental periods. Despite celecoxib diminishing the COX2 and IGFR1 levels, GTN presented higher action spectrum considering the decrease of a greater molecular number involved in the proliferative and inflammatory processes in prostatic cancer. Goniothalamin attenuated the pro-inflammatory response in TRAMP prostatic microenvironment, delaying prostate cancer (PCa) progression. Celecoxib treatment was efficient in the regulation of COX2 in the TRAMP mice, mainly in the advanced disease grade. Finally, we concluded that inflammatory process control in early grades of PCa was crucial for the downregulation of the signaling pathways involved in the proliferative processes in advanced cancer grades.

Observer Based Adaptive Fuzzy Controller with Modulated Membership Functions for Nonlinear System

An observer based adaptive fuzzy controller for nonlinear system is proposed. Parameters in the proposed adaptive fuzzy controller are tuned on-line by the genetic algorithm (GA). For on-line tuning, a parsimonious parameterization scheme for fuzzy controller called orthogonal modulated membership functions (mmf)35 is utilized. A simplified GA called micro GA that greatly improves the learning efficiency is applied. The valid range of mmf parameters will be proved in this paper. With the valid range of mmf parameters, the search by MGA for the optimal parameterization of the adaptive fuzzy controller is more focused resulting in fast convergence to the optimal solutions. A Lyapunov theorem based supervisory control is added to the fuzzy controller assuring that close loop stability is always maintained for the adaptive fuzzy controller during the learning process.