Modeling and control of non-ideally mixed bioreactors

Mixing plays a substantial role in determining the overall performance of a bioreactor. Well mixing in bioreactor, especially for ethanolic fermentation process is important for the homogenization of miscible and immiscible liquids, gas dispersion and suspension of solid particles. Improper mixing will eventually affect the biological and kinetics reactions occurring in the bioreactor and subsequently deteriorate the bioreactor performance. Currently, most modeling and control applications of bioreactors have been devoted to ideally mixed assumption, for simplicity. This is not realistic in practical applications. Furthermore, the strength and accuracy of the bioreactor models reflect their performance and subsequently its control strategy. Therefore, it is vital to consider the imperfect mixing for the control of bioreactor.In this study, a batch, micro-aerobic bioreactor for ethanolic fermentation process will be considered for modeling. Up to date, not much study has been conducted in exploiting the mixing mechanism for controlling this type of bioreactor. Traditionally, only the bioreactor conditions such as temperature and pH are controlled for such a batch bioreactor. Other parameters, such as aeration rate and stirrer speed are not used to control the bioreactor. Thus, it is difficult to improve the bioreactor performance as the bioreactor performance is less sensitive to both temperature and pH than to the mixing mechanism. However, the mixing behaviour of the bioreactor needs to be captured if we are to employ both aeration rate and stirrer speed for the control of such a batch bioreactor.It is known that aeration rate and stirrer speed could significantly affect the biological and kinetics reactions. Therefore, both aeration rate and stirrer speed are suggested in this work as manipulated variables in the modeling of batch bioreactor. Thus, with this approach the ideally mixed assumption will be relaxed.The models proposed will be implemented for control studies. New control strategies will be established for continuous bioreactor, whereby dilution rate and substrate concentration are considered as disturbance variables and both aeration rate and stirrer speed are suggested as manipulated variables. With this approach, the practicability of the proposed models could be investigated.The aims of this research have therefore been as follows: 1. To experimentally study the impact of aeration rate and stirrer speed on the bioreactor performances, i.e. yield and productivity. 2. To develop an integrated bioreactor model to allow us to employ the aeration rate and stirrer speed as manipulated variables for control design. 3. To establish new control strategies for bioreactor without the ideally mixed assumption.A systematic approach has been proposed to develop the non-ideally mixed bioreactor model and to design the control strategy of the lab-scale fermentation process. Three modeling approaches are employed, i.e. data-based, kinetics hybrid and kinetics multi-scale models for the analysis of the impacts of both aeration rate and stirrer speed on the performance of bioreactor. Using the three models, the aeration rate and stirrer speed are also used to analyze the mixing mechanism in the bioreactor.Furthermore, new control strategies are then proposed for the bioreactor. By using the proposed control strategies, the effect of both aeration rate and stirrer speed on the overall performance could be analyzed in the face of disturbances on other process parameters. Furthermore, the stability and achievable performance of the control strategies could be compared for different models. Hence, the proposed control strategies would lead to a better operation of the bioreactor.The study highlighted the following main findings: 1. It is identified that both aeration rate and stirrer speed could affect significantly the overall performance of the bioreactor. Therefore, both aeration rate and stirrer speed rather than temperature and pH could be used as manipulated variables for controlling the bioreactor. The ideally mixed assumption is relaxed where the mixing mechanism of the bioreactor is included in the proposed model.2. The main issue in modeling is the complexity of the microbial reactions and kinetics of the bioreactor performance for the non-ideally mixed behaviour of the bioreactor. Thus, it is important to identify the main reactions and kinetics which actually affect the bioreactor performance. In this study, Monod’s kinetics has been employed with the implementation of both aeration rate and stirrer speed. It is shown that the kinetics multi-scale model demonstrated good predictions of the mixing mechanism of bioreactor. Different conditions of aeration rate and stirrer speed influence the mixing mechanism and thus, contribute to the dynamics and kinetics within the bioreactor. These show that both aeration rate and stirrer speed play important role in studying the non-ideally mixed mechanism of the bioreactor.3. Optimization results, however, suggest that the kinetics hybrid model gives the most comparable values of maximum yield and productivity. Thus, this model is suggested for the determination of the optimum conditions of the bioreactor operation due to its simplicity in model construction, as compared to the kinetics multi-scale model.4. The control strategy of bioreactor using the data-based model does not always produce good performance, especially in the face of large disturbances. This implies that the use of models with ideally mixed assumptions would not always give good overall performance. Therefore, the controllability of the bioreactor performance is further improved with the implementation of the proposed non-ideally mixed bioreactor model. It is observed that both databased and kinetics hybrid models are able to keep the controlled variables in their set-point values by manipulating both aeration rate and stirrer speed for low disturbance changes.Hence, this research contributes on the understanding of mixing phenomena in micro-aerobic fermentation process from which a set of optimal operational conditions and control strategies to enhance its performance are developed

An index of syntactic development for Cantonese-Chinese preschool children

This research study aimed to develop an index of syntactic development for Cantonese-speaking children. Language samples taken from 14 normal children aged from 4;1 to 5;0, 16 normal children aged from 5;1 to 6;5 and 15 SLI children aged from 5;1 to 6;4 were analyzed and credited according to the framework developed. Normal children aged from 4;1 to 5;0 performed poorer on the index than those aged from 5;1 to 6;5 with the same clinical status. Children with language difficulty performed poorer than their normal age peers on the index as well. The index was validated against MLU and the two indices moderately correlated with each other. A linear combination of age, D and the index was entered into discriminant analysis, yielding a classification accuracy of 86.7% of all the children. The index was found to be a potentially useful clinical marker of SLI yet replication is needed to confirm the findings. Further modification of the index was discussed. The age and language growth sensitivity of MLU was discussed as well.published_or_final_versionSpeech and Hearing SciencesBachelorBachelor of Science in Speech and Hearing Science

Analysis of Ten Generations of Selection for Residual Feed Intake in Yorkshire Pigs

Ten generations (G) of divergent selection for residual feed intake (RFI) was practiced in Yorkshire pigs. This study shows that feed efficiency based on RFI was moderately heritable and responded to selection. Pigs selected for increased feed efficiency from the low RFI line ate less, grew slightly slower, and were leaner than pigs from the high RFI line. Thus, the results of this study show that selection for decreased RFI can improve feed efficiency and can be included in an economic selection index in addition to growth for reducing feed cost

Subtle Signals: Video-based Detection of Infant Non-nutritive Sucking as a Neurodevelopmental Cue

Non-nutritive sucking (NNS), which refers to the act of sucking on a pacifier, finger, or similar object without nutrient intake, plays a crucial role in assessing healthy early development. In the case of preterm infants, NNS behavior is a key component in determining their readiness for feeding. In older infants, the characteristics of NNS behavior offer valuable insights into neural and motor development. Additionally, NNS activity has been proposed as a potential safeguard against sudden infant death syndrome (SIDS). However, the clinical application of NNS assessment is currently hindered by labor-intensive and subjective finger-in-mouth evaluations. Consequently, researchers often resort to expensive pressure transducers for objective NNS signal measurement. To enhance the accessibility and reliability of NNS signal monitoring for both clinicians and researchers, we introduce a vision-based algorithm designed for non-contact detection of NNS activity using baby monitor footage in natural settings. Our approach involves a comprehensive exploration of optical flow and temporal convolutional networks, enabling the detection and amplification of subtle infant-sucking signals. We successfully classify short video clips of uniform length into NNS and non-NNS periods. Furthermore, we investigate manual and learning-based techniques to piece together local classification results, facilitating the segmentation of longer mixed-activity videos into NNS and non-NNS segments of varying duration. Our research introduces two novel datasets of annotated infant videos, including one sourced from our clinical study featuring 19 infant subjects and 183 hours of overnight baby monitor footage

Arsenic Biotransformation as a Cancer Promoting Factor by Inducing DNA Damage and Disruption of Repair Mechanisms

Chronic exposure to arsenic in drinking water poses a major global health concern. Populations exposed to high concentrations of arsenic-contaminated drinking water suffer serious health consequences, including alarming cancer incidence and death rates. Arsenic is biotransformed through sequential addition of methyl groups, acquired from s-adenosylmethionine (SAM). Metabolism of arsenic generates a variety of genotoxic and cytotoxic species, damaging DNA directly and indirectly, through the generation of reactive oxidative species and induction of DNA adducts, strand breaks and cross links, and inhibition of the DNA repair process itself. Since SAM is the methyl group donor used by DNA methyltransferases to maintain normal epigenetic patterns in all human cells, arsenic is also postulated to affect maintenance of normal DNA methylation patterns, chromatin structure, and genomic stability. The biological processes underlying the cancer promoting factors of arsenic metabolism, related to DNA damage and repair, will be discussed here

Arsenic Exposure and the Induction of Human Cancers

Arsenic is a metalloid, that is, considered to be a human carcinogen. Millions of individuals worldwide are chronically exposed through drinking water, with consequences ranging from acute toxicities to development of malignancies, such as skin and lung cancer. Despite well-known arsenic-related health effects, the molecular mechanisms involved are not fully understood; however, the arsenic biotransformation process, which includes methylation changes, is thought to play a key role. This paper explores the relationship of arsenic exposure with cancer development and summarizes current knowledge of the potential mechanisms that may contribute to the neoplastic processes observed in arsenic exposed human populations

Climate Diagnostics of the Extreme Floods in Peru During Early 2017

From January through March 2017, a series of extreme precipitation events occurred in coastal Peru, causing severe floods with hundreds of human casualties and billions of dollars in economic losses. The extreme precipitation was a result of unusually strong recurrent patterns of atmospheric and oceanic conditions, including extremely warm coastal sea surface temperatures (SST) and weakened trade winds. These climatic features and their causal relationship with the Peruvian precipitation were examined. Diagnostic analysis and model experiments suggest that an atmospheric forcing in early 2017, which was moderately linked to the Trans-Niño Index (TNI), initiated the local SST warming along coastal Peru that later expanded to the equator. In January 2017, soil moisture was increased by an unusual expansion of Amazonian rainfall. By March, localized and robust SST warming provided positive feedback to the weakening of the trade winds, leading to increased onshore wind and a subsequent enhancement in rainfall. The analysis points to a tendency towards more frequent and stronger variations in the water vapor flux convergence along the equator, which is associated with the increased precipitation in coastal Peru