Hector Budman

Identifying fouling events in a membrane-based drinking water treatment process using principal component analysis of fluorescence excitation-emission matrices

The identification of key foulants and the provision of early warning of high fouling events for drinking water treatment membrane processes is crucial for the development of effective countermeasures to membrane fouling, such as pretreatment. Principal foulants include organic, colloidal and particulate matter present in the membrane feed water. In this research, principal component analysis (PCA) of fluorescence excitation-emission matrices (EEMs) was identified as a viable tool for monitoring the performance of pre-treatment stages (in this case biological filtration), as well as ultrafiltration (UF) and nanofiltration (NF) membrane systems. In addition, fluorescence EEM-based principal component (PC) score plots, generated using the fluorescence EEMs obtained after just 1hour of UF or NF operation, could be related to high fouling events likely caused by elevated levels of particulate/colloid-like material in the biofilter effluents. The fluorescence EEM-based PCA approach presented here is sensitive enough to be used at low organic carbon levels and has potential as an early detection method to identify high fouling events, allowing appropriate operational countermeasures to be taken.

Reversible and irreversible low-pressure membrane foulants in drinking water treatment: Identification by principal component analysis of fluorescence EEM and mitigation by biofiltration pretreatment

With the increased use of membranes in drinking water treatment, fouling--particularly the hydraulically irreversible type--remains the main operating issue that hinders performance and increases operational costs. The main challenge in assessing fouling potential of feed water is to accurately detect and quantify feed water constituents responsible for membrane fouling. Utilizing fluorescence excitation-emission matrices (EEM), protein-like substances, humic and fulvic acids, and particulate/colloidal matter can be detected with high sensitivity in surface waters. The application of principal component analysis to fluorescence EEMs allowed estimation of the impact of surface water constituents on reversible and irreversible membrane fouling. This technique was applied to experimental data from a two year bench-scale study that included thirteen experiments investigating the fouling potential of Grand River water (Ontario, Canada) and the effect of biofiltration pre-treatment on the level of foulants during ultrafiltration (UF). Results showed that, although the content of protein-like substances in this membrane feed water (=biofiltered natural water) was much lower than commonly found in wastewater applications, the content of protein-like substances was still highly correlated with irreversible fouling of the UF membrane. In addition, there is evidence that protein-like substances and particulate/colloidal matter formed a combined fouling layer, which contributed to both reversible and irreversible fouling. It is suggested that fouling transitions from a reversible to an irreversible regime depending on feed composition and operating time. Direct biofiltration without prior coagulant addition reduced the protein-like content of the membrane feed water which in turn reduced the irreversible fouling potential for UF membranes. Biofilters also decreased reversible fouling, and for both types of fouling higher biofilter contact times were beneficial.

Comparative study of black-box and hybrid estimation methods in fed-batch fermentation

A neural network based softsensor is proposed for a PHB fed-batch fermentation process. The softsensor is designed to estimate the biomass concentration on-line. The design is based on the following model structures: 1. a feedforward neural network, 2. a RBFN (radial basis function neural network) and 3. hybrid models composed of either feedforward or RBFN neural network and the a priori known dilution term of the mass balance equations. The different designs are experimentally implemented and compared using Alcaligenes eutrophus as a model fed-batch system. Additionally, the possibility of directly inferring the substrate (glucose) concentration from the estimated biomass was investigated by assessing the variability of the corresponding yield coefficient. The combination of the neural network model and mechanistic differential equation provided the best results. Because of the variability in the yield coefficient, substrate concentration could not be inferred directly.

Integration of design and control for chemical processes: A review of the literature and some recent results ☆

Abstract This paper presents a literature review on the integration of control and design problem followed by the description of two new methodologies that have been recently applied to achieve this integration. These methods are based on mathematical tools that have been commonly used for the design of robust controllers. Using these tools, the integration of the control and design problem can be formulated as a nonlinear constrained optimization problem that is significantly less computationally demanding than previously proposed dynamic optimization-based optimization methods. A mixing tank process is used to illustrate the proposed methodologies. Part of the material included in this manuscript was presented as a keynote lecture at the DYCOPS 2007 conference ( Ricardez Sandoval et al., 2007 ).

Assessing the role of feed water constituents in irreversible membrane fouling of pilot-scale ultrafiltration drinking water treatment systems.

Fluorescence excitation-emission matrix (EEM) approach together with principal component analysis (PCA) was used for assessing hydraulically irreversible fouling of three pilot-scale ultrafiltration (UF) systems containing full-scale and bench-scale hollow fiber membrane modules in drinking water treatment. These systems were operated for at least three months with extensive cycles of permeation, combination of back-pulsing and scouring and chemical cleaning. The principal component (PC) scores generated from the PCA of the fluorescence EEMs were found to be related to humic substances (HS), protein-like and colloidal/particulate matter content. PC scores of HS- and protein-like matter of the UF feed water, when considered separately, showed reasonably good correlations with the rate of hydraulically irreversible fouling for long-term UF operations. In contrast, comparatively weaker correlations for PC scores of colloidal/particulate matter and the rate of hydraulically irreversible fouling were obtained for all UF systems. Since, individual correlations could not fully explain the evolution of the rate of irreversible fouling, multi-linear regression models were developed to relate the combined effect of HS-like, protein-like and colloidal/particulate matter PC scores to the rate of hydraulically irreversible fouling for each specific UF system. These multi-linear regression models revealed significant individual and combined contribution of HS- and protein-like matter to the rate of hydraulically irreversible fouling, with protein-like matter generally showing the greatest contribution. The contribution of colloidal/particulate matter to the rate of hydraulically irreversible fouling was not as significant. The addition of polyaluminum chloride, as coagulant, to UF feed appeared to have a positive impact in reducing hydraulically irreversible fouling by these constituents. The proposed approach has applications in quantifying the individual and synergistic contribution of major natural water constituents to the rate of hydraulically irreversible membrane fouling and shows potential for controlling UF irreversible fouling in the production of drinking water.

Effect of temperature on the inhibition kinetics of phenol biodegradation by Pseudomonas putida Q5

The temperature-dependent performance of mixed-culture wastewater treatment processes may be strongly influenced by their content of psychrotrophic bacteria. In this work, the effect of temperature on cell growth and phenol biodegradation kinetics of the psychrotrophic bacterium Pseudomonas putida Q5 were determined using both batch and continuous cultures in the range of 10-25 degrees C. The Haldane equation was found to be the most suitable substrate-inhibition model for the specific growth rate. The Haldane parameters mu(max) and K(I) were best modeled by a square-root dependency on temperature. However, the Arrhenius model provided a better prediction of the temperature dependence of K(S). The variation of the yield constant with temperature also was studied experimentally. Comparisons with results of previous workers are presented.

A methodology for the simultaneous design and control of large-scale systems under process parameter uncertainty

This work presents a simultaneous design and control methodology for large-scale systems. The approach is based on the identification of an uncertain model from a first-principle process model. Using the identified uncertain model, a Structured Singular Value (SSV) analysis is used to estimate the realizations in the disturbance set that generates the worst-case variability and constraint violations. Then, simulations of the first-principle process model are performed with the critical disturbance profile as input to estimate the actual worst-case output variability and the worst-case variations in the process constraints. Since the proposed methodology is formulated as a nonlinear constrained optimization problem, it avoids the computationally expensive task of solving dynamic optimization problems, making it suitable for application to large-scale systems. The proposed methodology was tested on the Tennessee Eastman process to show that a redesign of the major process units in the process could significantly reduce the costs of this plant.

Metabolic flux-based modeling of mAb production during batch and fed-batch operations.

This paper proposes mathematical models that predict the physiology, growth behavior and productivity of hybridoma cells in both batch and fed-batch systems. Murine hybridoma 130-8F producing anti-F-glycoprotein monoclonal antibody was employed as a model system. A systematic approach based on metabolic flux analysis (MFA) was utilized to yield a dynamic model for predicting the concentration of significant metabolites over time. Correlation analysis was performed to formulate a Biomass Model for predicting cell concentration and viability as a function of the extracellular metabolite concentrations. The coefficients of the model equation were estimated by employing the Metropolis–Hastings algorithm. The Metabolites Model was combined with the Biomass Model to get an Integrated Model capable of predicting concentration values for substrates, extracellular metabolites, and viable and dead cell concentration by utilizing only starting concentrations as input. The prediction accuracy of the model was tested by using experimental data.

Dynamic metabolic modeling for a MAB bioprocess

Production of monoclonal antibodies (MAb) for diagnostic or therapeutic applications has become an important task in the pharmaceutical industry. The efficiency of high‐density reactor systems can be potentially increased by model‐based design and control strategies. Therefore, a reliable kinetic model for cell metabolism is required. A systematic procedure based on metabolic modeling is used to model nutrient uptake and key product formation in a MAb bioprocess during both the growth and post‐growth phases. The approach combines the key advantages of stoichiometric and kinetic models into a complete metabolic network while integrating the regulation and control of cellular activity. This modeling procedure can be easily applied to any cell line during both the cell growth and post‐growth phases. Quadratic programming (QP) has been identified as a suitable method to solve the underdetermined constrained problem related to model parameter identification. The approach is illustrated for the case of murine hybridoma cells cultivated in stirred spinners.

Effects of nutrient levels and average culture pH on the glycosylation pattern of camelid-humanized monoclonal antibody.

The impact of operating conditions on the glycosylation pattern of humanized camelid monoclonal antibody, EG2-hFc produced by Chinese hamster ovary (CHO) cells has been evaluated by a combination of experiments and modeling. Cells were cultivated under different levels of glucose and glutamine concentrations with the goal of investigating the effect of nutrient depletion levels and ammonia build up on the cell growth and the glycoprofiles of the monoclonal antibody (Mab). The effect of average pH reduction on glycosylation level during the entire culture time or during a specific time span was also investigated. The relative abundance of glycan structures was quantified by hydrophilic interaction liquid chromatography (HILIC) and the galactosylation index (GI) and the sialylation index (SI) were determined. Lower initial concentrations of glutamine resulted in lower glucose consumption and lower cell yield but increased GI and SI levels when compared to cultures started with higher initial glutamine levels. Similarly, reducing the average pH of culture resulted in lower growth but higher SI and GI levels. These findings indicate that there is a tradeoff between cell growth, resulting Mab productivity and the achievement of desirable higher glycosylation levels. A dynamic model, based on a metabolic flux analysis (MFA), is proposed to describe the metabolism of nutrients, cell growth and Mab productivity. Finally, existing software (GLYCOVIS) that describes the glycosylation pathways was used to illustrate the impact of extracellular species on the glycoprofiles.