Modeling of heat and mass transfer for solid state fermentation process in tray bioreactor

A mathematical model is developed to simulate oxygen consumption, heat generation and cell growth in solid state fermentation (SSF). The fungal growth on the solid substrate particles results in the increase of the cell film thickness around the particles. The model incorporates this increase in the biofilm size which leads to decrease in the porosity of the substrate bed and diffusivity of oxygen in the bed. The model also takes into account the effect of steric hindrance limitations in SSF. The growth of cells around single particle and resulting expansion of biofilm around the particle is analyzed for simplified zero and first order oxygen consumption kinetics. Under conditions of zero order kinetics, the model predicts upper limit on cell density. The model simulations for packed bed of solid particles in tray bioreactor show distinct limitations on growth due to simultaneous heat and mass transport phenomena accompanying solid state fermentation process. The extent of limitation due to heat and/or mass transport phenomena is analyzed during different stages of fermentation. It is expected that the model will lead to better understanding of the transport processes in SSF, and therefore, will assist in optimal design of bioreactors for SSF.

Supermacroporous Polymer-Based Cryogel Bioreactor for Monoclonal Antibody Production in Continuous Culture Using Hybridoma Cells

Cryogel matrices composed of different polymeric blends were synthesized, yielding a unique combination of hydrophilicity and hydrophobicity with the presence or absence of charged surface. Four such cryogel matrices composed of polyacrylamide-chitosan (PAAC), poly(N-isopropylacrylamide)-chitosan, polyacrylonitrile (PAN), and poly(N-isopropylacrylamide) were tested for growth of different hybridoma cell lines and production of antibody in static culture. All the matrices were capable for the adherence of hybridoma cell lines 6A4D7, B7B10, and H9E10 to the polymeric surfaces as well as for the efficient monoclonal antibody (mAb) production. PAAC proved to be relatively better in terms of both mAb production and cell growth. Further, PAAC cryogel was designed into three different formats, monolith, disks, and beads, and used as packing material for packed-bed bioreactor. Longterm cultivation of 6A4D7 cell line on PAAC cryogel scaffold in all the three formats could be successfully done for a period of 6 weeks under static conditions. Continuous packed-bed bioreactor was setup using 6A4D7 hybridoma cell line in the three reactor formats. The reactors ran continuously for a period of 60 days during which mAb production and metabolism of cells in the bioreactors were monitored periodically. The monolith bioreactor performed most efficiently over a period of 60 days and produced a total of 57.5 mg of antibody in the first 30 days (in 500 mL) with a highest concentration of 115 mu g mL(-1), which is fourfold higher than t-flask culture. The results demonstrate that appropriate chemistry and geometry of the bioreactor matrix for cell growth and immobilization can enhance the reactor productivity. (C) 2010 American Institute of Chemical Engineers Biotechnol. Prog., 27: 170-180, 2011

Adaptive optimization of continuous bioreactor using neural network model

An adaptive optimization algorithm using backpropogation neural network model for dynamic identification is developed. The algorithm is applied to maximize the cellular productivity of a continuous culture of baker's yeast. The robustness of the algorithm is demonstrated in determining and maintaining the optimal dilution rate of the continuous bioreactor in presence of disturbances in environmental conditions and microbial culture characteristics. The simulation results show that a significant reduction in time required to reach optimal operating levels can be achieved using neural network model compared with the traditional dynamic linear input-output model. The extension of the algorithm for multivariable adaptive optimization of continuous bioreactor is briefly discussed.

Transient analysis of mammalian cell growth in hollow fibre bioreactor

A mathematical model describing the dynamics of mammalian cell growth in hollow fibre bioreactor operated in closed shell mode is developed. Mammalian cells are assumed to grow as an expanding biofilm in the extra-capillary space surrounding the fibre. Diffusion is assumed to be the dominant process in the radial direction while axial convection dominates in the lumen of the bioreactor. The transient simulation results show that steep gradients in the cell number are possible under the condition of substrate limitation. The precise conditions which result in nonuniform growth of cells along the length of the bioreactor are delineated. The effect of various operating conditions, such as substrate feed rate, length of the bioreactor and diffusivity of substrate in different regions of the bioreactor, on the bioreactor performance are evaluated in terms of time required to attain the steady-state. The rime of growth is introduced as a measure of effectiveness factor for the bioreactor and is found to be dependent on two parameters, a modified Peclet number and a Thiele modulus. Diffusion, reaction and/or convection control regimes are identified based on these two parameters. The model is further extended to include dual substrate growth limitations, and the relative growth limiting characteristics of two substrates are evaluated. (C) 1997 Elsevier Science Ltd.

Evolutionary Algorithm for the Determination of Optimal Mode of Bioreactor Operation

Stirred tank bioreactors, employed in the production of a variety of biologically active chemicals, are often operated in batch, fed-batch, and continuous modes of operation. The optimal design of bioreactor is dependent on the kinetics of the biological process, as well as the performance criteria (yield, productivity, etc.) under consideration. In this paper, a general framework is proposed for addressing the two key issues related to the optimal design of a bioreactor, namely, (i) choice of the best operating mode and (ii) the corresponding flow rate trajectories. The optimal bioreactor design problem is formulated with initial conditions and inlet and outlet flow rate trajectories as decision variables to maximize more than one performance criteria (yield, productivity, etc.) as objective functions. A computational methodology based on genetic algorithm approach is developed to solve this challenging multiobjective optimization problem with multiple decision variables. The applicability of the algorithm is illustrated by solving two challenging problems from the bioreactor optimization literature.

Effect of leachate recirculation and extent of degradation on the stability of bioreactor landfill slopes

The stability of a bioreactor landfill slope is influenced by the quantity and method of leachate recirculation as well as on the degree of decomposition. Other factors include properties variation of waste material and geometrical configurations, i.e., height and slope of landfills. Conventionally, the stability of slopes is evaluated using factor of safety approach, in which the variability in the engineering properties of MSW is not considered directly and stability issues are resolved from past experiences and good engineering judgments. On the other hand, probabilistic approach considers variability in mathematical framework and provides stability in a rational manner that helps in decision making. The objective of the present study is to perform a parametric study on the stability of a bioreactor landfill slope in probabilistic framework considering important influencing factors, such as, variation in MSW properties, amount of leachate recirculation, and age of degradation, in a systematic manner. The results are discussed in the light of existing relevant regulations, design and operation issues.

Influence of Spatial Variation of Hydraulic Conductivity of Municipal Solid Waste on Performance of Bioreactor Landfill

The current study analyzes the leachate distribution in the Orchard Hills Landfill, Davis Junction, Illinois, using a two-phase flow model to assess the influence of variability in hydraulic conductivity on the effectiveness of the existing leachate recirculation system and its operations through reliability analysis. Numerical modeling, using finite-difference code, is performed with due consideration to the spatial variation of hydraulic conductivity of the municipal solid waste (MSW). The inhomogeneous and anisotropic waste condition is assumed because it is a more realistic representation of the MSW. For the reliability analysis, the landfill is divided into 10 MSW layers with different mean values of vertical and horizontal hydraulic conductivities (decreasing from top to bottom), and the parametric study is performed by taking the coefficients of variation (COVs) as 50, 100, 150, and 200%. Monte Carlo simulations are performed to obtain statistical information (mean and COV) of output parameters of the (1) wetted area of the MSW, (2) maximum induced pore pressure, and (3) leachate outflow. The results of the reliability analysis are used to determine the influence of hydraulic conductivity on the effectiveness of the leachate recirculation and are discussed in the light of a deterministic approach. The study is useful in understanding the efficiency of the leachate recirculation system. (C) 2013 American Society of Civil Engineers.

Ferrous iron oxidation by foam immobilized Acidithiobacillus ferrooxidans: Experiments and modeling

Ferrous iron bio-oxidation by Acidithiobacillus ferrooxidans immobilized on polyurethane foam was investigated. Cells were immobilized on foams by placing them in a growth environment and fully bacterially activated polyurethane foams (BAPUFs) were prepared by serial subculturing in batches with partially bacterially activated foam (pBAPUFs). The dependence of foam density on cell immobilization process, the effect of pH and BAPUF loading on ferrous oxidation were studied to choose operating parameters for continuous operations. With an objective to have high cell densities both in foam and the liquid phase, pretreated foams of density 50 kg/m3 as cell support and ferrous oxidation at pH 1.5 to moderate the ferric precipitation were preferred. A novel basket-type bioreactor for continuous ferrous iron oxidation, which features a multiple effect of stirred tank in combination with recirculation, was designed and operated. The results were compared with that of a free cell and a sheet-type foam immobilized reactors. A fivefold increase in ferric iron productivity at 33.02 g/h/L of free volume in foam was achieved using basket-type bioreactor when compared to a free cell continuous system. A mathematical model for ferrous iron oxidation by Acidithiobacillus ferrooxidans cells immobilized on polyurethane foam was developed with cell growth in foam accounted by an effectiveness factor. The basic parameters of simulation were estimated using the experimental data on free cell growth as well as from cell attachment to foam under nongrowing conditions. The model predicted the phase of both oxidation of ferrous in shake flasks by pBAPUFs as well as by fully activated BAPUFs for different cell loadings in foam. Model for stirred tank basket bioreactor predicted within 5% both transient and steady state of the experiments closely for the simulated dilution rates. Bio-oxidation at high Fe2+ concentrations were simulated with experiments when substrate and product inhibition coefficients were factored into cell growth kinetics.

Pareto-optimal solutions for multi-objective optimization of fed-batch bioreactors using nondominated sorting genetic algorithm

Many optimal control problems are characterized by their multiple performance measures that are often noncommensurable and competing with each other. The presence of multiple objectives in a problem usually give rise to a set of optimal solutions, largely known as Pareto-optimal solutions. Evolutionary algorithms have been recognized to be well suited for multi-objective optimization because of their capability to evolve a set of nondominated solutions distributed along the Pareto front. This has led to the development of many evolutionary multi-objective optimization algorithms among which Nondominated Sorting Genetic Algorithm (NSGA and its enhanced version NSGA-II) has been found effective in solving a wide variety of problems. Recently, we reported a genetic algorithm based technique for solving dynamic single-objective optimization problems, with single as well as multiple control variables, that appear in fed-batch bioreactor applications. The purpose of this study is to extend this methodology for solution of multi-objective optimal control problems under the framework of NSGA-II. The applicability of the technique is illustrated by solving two optimal control problems, taken from literature, which have usually been solved by several methods as single-objective dynamic optimization problems. (C) 2004 Elsevier Ltd. All rights reserved.

Modeling and optimization of a natural convection-based agricultural dryer

This study presents development of a computational fluid dynamic (CFD) model to predict unsteady, two-dimensional temperature, moisture and velocity distributions inside a novel, biomass-fired, natural convection-type agricultural dryer. Results show that in initial stages of drying, when material surface is wet and moisture is easily available, moisture removal rate from surface depends upon the condition of drying air. Subsequently, material surface becomes dry and moisture removal rate is driven by diffusion of moisture from inside to the material surface. An optimum 9-tray configuration is found to be more efficient than for the same mass of material and volume of dryer. A new configuration of dryer, mainly to explore its potential to increasing uniformity in drying across all trays, is also analyzed. This configuration involves diverting a portion of hot air before it enters over the first tray and is supplied directly at an intermediate location in the dryer. Uniformity in drying across trays has increased for the kind of material simulated.

Biomineralization and Biobeneficiation of Bauxite

Role of indigenous microbes in the formation and conversion of bauxite minerals is illustrated. Many types of microorganisms such as fungi, heterotrophic and autotrophic bacteria and yeasts inhabit bauxite ore deposits bringing about biogenesis and biomineraliztion. Organisms capable of iron oxidation and reduction and solubilising calcium carbonate and silica can be isolated from bauxite deposits and are used to bring about selective mineral beneficiation to remove iron, calcium and silica. Use of Paenibacillus polymyxa in the efficient removal of calcium from low grade bauxites is demonstrated through bioreactor technology. Similarly, for iron removal from bauxite, iron-reducing bacteria can be used. Silicate bacteria aid in selective silica solubilisation to control alumina: silica ratios. Microorganisms can also be used to bring about environmental control with respect to red mud disposal through bioremediation technology.