To ensure effective detection of bio-particles, it is crucial to understand the effects of collection method and archiving conditions on the survivability of bioaerosols, consequently, the survivability of the spore-forming Bacillus globigii (BG) and MG1655 Escherichia coli (E. coli), was determined after collection. The survivability was defined as the culturable fraction of the archived bacteria/culturable fraction of the as-collected bacteria. The bacteria were aerosolized for up to four days at room temperature (RT, 25 degrees C) and at 4 degrees C and collected in a 100 L/min wetted wall cyclone (WWC) and a 12.5 L/min SKC BioSampler. Aqueous solutions of 0.01% Tween-20 and 30% Ethylene Glycol (EG), with or without 0.5% ovalbumin (OA), were used as the collection fluids. Antifoam B (A-F), at a concentration of 0.2% (V:V) was added to the BG samples containing OA. In general, samples archived at 4 degrees C showed higher survivability than at RT. The survivability were more stable in EG than in Tween-20 especially for BG, very likely due to the surfactant effect of the Tween-20, which would remove the spore coat and initiate germination. In the WWC, adding OA significantly increased the survivability of BG in EG and in Tween-20, especially at RT. Similar effect of OA was found for E. coli samples stored in EG, suggesting that OA might be beneficial in maintaining the survivability. Adding A-F increased the survivability of BG in EG. In the SKC, neither the addition of OA nor A-F seems to have a beneficial effect on the survivability of the spores in EG samples. The best collection fluid for maintaining survivability in the WWC is EG+A-F for BG, and EG+OA for E. coli. However, in the SKC, EG is the best for BG collection and Tween-20 for E. coli. Viability transfer ratios, VTR, (cells surviving collection at time zero/viable cells aerosolized) were calculated for both devices. A performance ratio was calculated as the VTR of the WWC/VTR of the SKC. The geometric mean of the performance ratio is 1.51+/-0.83 for BG and 2.60+/-0.16 for E. coli, indicating that viability transfer ratio of the WWC is typically higher than that of the SKC.

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Biosurfactants are naturally occurring amphiphiles with potential for use as alternatives to traditional petrochemical and oleochemical surfactants. The unique properties of biosurfactants, including their biodegradability and tolerance of a wide range of temperature and pH, make their use in a range of novel applications attractive. Currently the wider ultilisation of biosurfactants is hindered by a lack of economically viable production routes, with downstream processing presenting a significant challenge. This thesis presents an investigation into the production of HFBII, a hydrophobin protein, using an adsorptive bubble separation technique called foam fractionation for in situ recovery of the biosurfactant. The effects of foaming on the production of HFBII by fermentation were investigated at two different scales. Foaming behaviour was characterised in standard terms of the product enrichment and recovery achieved. Additional specific attention was given to the rate at which foam, product and biomass overflowed from the fermentation system in order to assess the utility of foam fractionation for HFBII recovery. HFBII was expressed as an extracellular product during fed batch fermentations with a genetically modified strain of Saccharomyces cerevisiae, which were carried out with and without antifoam. In the presence of antifoam HFBII production is shown to be largely unaffected by process scale, with similar yields of HFBII on dry matter obtained. More variation in HFBII yield was observed between fermentations without antifoam. In fermentations without antifoam a maximum HFBII enrichment in the foam phase of 94.7 was measured with an overall enrichment of 54.6 at a recovery of 98.1%, leaving a residual HFBII concentration of 5.3 mg L-1 in the fermenter. It is also shown that uncontrolled foaming reduced the concentration of biomass in the fermenter vessel, affecting total production. This series of fermentation experiments illustrates the potential for the application of foam fractionation for efficient in situ recovery of HFBII, through simultaneous high enrichment and recovery which are greater than those reported for similar systems. After the suitability of foam fractionation was demonstrated a novel apparatus design was developed for continuously recovering extracellular biosurfactants from fermenters. The design allows for the operating conditions of the foam fractionation process, feed rate and airflow rate, to be chosen independently of the fermentation parameters. Optimal conditions can then be established for each process, such as the aeration rate required to meet the biological oxygen demand of the cell population. The recirculating foam fractionation process was tested on HFBII producing fermentations. It is shown that by using foam fractionation to strip HFBII from fermentation broth in situ the amount of uncontrolled overflowing from the fermenter was greatly reduced from 770.0 g to 44.8 g, compared to fermentations without foam fractionation. Through optimisation of the foam column operating conditions the proportion of dry matter retained in the fermenter was increased from 88% to 95%, in contrast to a dry matter retention of 66% for fermentation without the new design. With the integrated foam fractionation process a HFBII recovery of 70% was achieved at an enrichment of 6.6. This work demonstrates the utility of integrated foam fractionation in minimising uncontrolled foaming in fermenters whilst recovering an enriched product. This integrated production and separation process has the potential to facilitate improved biosurfactant production, currently a major barrier to their wider use.

Chemical engineers have routinely used computational tools for modeling, optimizing, and debottlenecking chemical processes. Because of the advances in computational science over the past decade, multivariate statistics and machine learning have become an integral part of the computerization of chemical processes. In this research, we look into using multivariate statistics, machine learning tools, and their combinations through a series of case studies including a case with a successful industrial deployment of machine learning models for fermentation. We use both commercially-available software tools, Aspen ProMV and Python, to demonstrate the feasibility of the computational tools. This work demonstrates a novel application of ensemble-based machine learning methods in bioprocessing, particularly for the prediction of different fermenter types in a fermentation process (to allow for successful data integration) and the prediction of the onset of foaming. We apply two ensemble frameworks, Extreme Gradient Boosting (XGBoost) and Random Forest (RF), to build classification and regression models. Excessive foaming can interfere with the mixing of reactants and lead to problems, such as decreasing effective reactor volume, microbial contamination, product loss, and increased reaction time. Physical modeling of foaming is an arduous process as it requires estimation of foam height, which is dynamic in nature and varies for different processes. In addition to foaming prediction, we extend our work to control and prevent foaming by allowing data-driven ad hoc addition of antifoam using exhaust differential pressure as an indicator of foaming. We use large-scale real fermentation data for six different types of sporulating microorganisms to predict foaming over multiple strains of microorganisms and build exploratory time-series driven antifoam profiles for four different fermenter types. In order to successfully predict the antifoam addition from the large-scale multivariate dataset (about half a million instances for 163 batches), we use TPOT (Tree-based Pipeline Optimization Tool), an automated genetic programming algorithm, to find the best pipeline from 600 other pipelines. Our antifoam profiles are able to decrease hourly volume retention by over 53% for a specific fermenter. A decrease in hourly volume retention leads to an increase in fermentation product yield. We also study two different cases associated with the manufacturing of polyolefins, particularly LDPE (low-density polyethylene) and HDPE (high-density polyethylene). Through these cases, we showcase the usage of machine learning and multivariate statistical tools to improve process understanding and enhance the predictive capability for process optimization. By using indirect measurements such as temperature profiles, we demonstrate the viability of such measures in the prediction of polyolefin quality parameters, anomaly detection, and statistical monitoring and control of the chemical processes associated with a LDPE plant. We use dimensionality reduction, visualization tools, and regression analysis to achieve our goals. Using advanced analytical tools and a combination of algorithms such as PCA (Principal Component Analysis), PLS (Partial Least Squares), Random Forest, etc., we identify predictive models that can be used to create inferential schemes. Soft-sensors are widely used for on-line monitoring and real-time prediction of process variables. In one of our cases, we use advanced machine learning algorithms to predict the polymer melt index, which is crucial in determining the product quality of polymers. We use real industrial data from one of the leading chemical engineering companies in the Asia-Pacific region to build a predictive model for a HDPE plant. Lastly, we show an end-to-end workflow for deep learning on both industrial and simulated polyolefin datasets. Thus, using these five cases, we explore the usage of advanced machine learning and multivariate statistical techniques in the optimization of chemical and biochemical processes. The recent advances in computational hardware allow engineers to design such data-driven models, which enhances their capacity to effectively and efficiently monitor and control a process. We showcase that even non-expert chemical engineers can implement such machine learning algorithms with ease using open-source or commercially available software tools. / Doctor of Philosophy / Most chemical and biochemical processes are equipped with advanced probes and connectivity sensors that collect large amounts of data on a daily basis. It is critical to manage and utilize the significant amount of data collected from the start and throughout the development and manufacturing cycle. Chemical engineers have routinely used computational tools for modeling, designing, optimizing, debottlenecking, and troubleshooting chemical processes. Herein, we present different applications of machine learning and multivariate statistics using industrial datasets. This dissertation also includes a deployed industrial solution to mitigate foaming in commercial fermentation reactors as a proof-of-concept (PoC). Our antifoam profiles are able to decrease volume loss by over 53% for a specific fermenter. Throughout this dissertation, we demonstrate applications of several techniques like ensemble methods, automated machine learning, exploratory time series, and deep learning for solving industrial problems. Our aim is to bridge the gap from industrial data acquisition to finding meaningful insights for process optimization.

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