Multicriteria optimization of gluconic acid production using net flow

The biochemical process industry is often confronted with the challenge of making decisions in an atmosphere of multiple and conflicting objectives. Recent innovations in the field of operations research and systems science have yielded rigorous multicriteria optimization techniques that can be successfully applied to the field of biochemical engineering. These techniques incorporate the expert's experience into the optimization routine and provide valuable information about the zone of possible solutions. This paper presents a multicriteria optimization strategy that generates a Pareto domain, given a set of conflicting objective criteria, and determines the optimal operating region for the production of gluconic acid using the net flow method (NFM). The objective criteria include maximizing the productivity and concentration of gluconic acid, while minimizing the residual substrate. Three optimization strategies are considered. The first two strategies identify the optimal operating region for the process inputs. The results yielded an acceptable compromise between productivity, gluconic acid concentration and residual substrate concentration. Fixing the process inputs representing the batch time, initial substrate concentration and initial biomass equal to their optimal values, the remaining simulations were used to study the sensitivity of the optimum operating region to changes in the oxygen mass transfer coefficient, K(L) a, by utilizing a multi-level K(L) a strategy. The results show that controlling K(L) a during the reaction reduced the production of biomass, which in turn resulted in increased productivity and concentration of gluconic acid above that of a fixed K(L) a.     

Hybrid physics-based and data-driven modeling for bioprocess online simulation and optimization

Model-based online optimization has not been widely applied to bioprocesses due to the challenges of modeling complex biological behaviors, low-quality industrial measurements, and lack of visualization techniques for ongoing processes. This study proposes an innovative hybrid modeling framework which takes advantages of both physics-based and data-driven modeling for bioprocess online monitoring, prediction, and optimization. The framework initially generates high-quality data by correcting raw process measurements via a physics-based noise filter (a generally available simple kinetic model with high fitting but low predictive performance); then constructs a predictive data-driven model to identify optimal control actions and predict discrete future bioprocess behaviors. Continuous future process trajectories are subsequently visualized by re-fitting the simple kinetic model (soft sensor) using the data-driven model predicted discrete future data points, enabling the accurate monitoring of ongoing processes at any operating time. This framework was tested to maximize fed-batch microalgal lutein production by combining with different online optimization schemes and compared against the conventional open-loop optimization technique. The optimal results using the proposed framework were found to be comparable to the theoretically best production, demonstrating its high predictive and flexible capabilities as well as its potential for industrial application.     

Process optimization of continuous gluconic acid fermentation by isolated yeast-like strains of Aureobasidium pullulans

This study was focused on the optimization of a new fermentation process for continuous gluconic acid production by the isolated yeast-like strain Aureobasidium pullulans DSM 7085 (isolate 70). Operational fermentation parameters were optimized in chemostat cultures, using a defined glucose medium. Different optima were found for growth and gluconic acid production for each set of operation parameters. Highest productivity was recorded at pH values between 6.5 and 7.0 and temperatures between 29 and 31 degrees C. A gluconic acid concentration higher than 230 g/L was continuously produced at residence times of 12 h. A steady state extracellular gluconic acid concentration of 234 g/L was measured at pH 6.5. 122% air saturation yielded the highest volumetric productivity and product concentration. The biomass-specific productivity increased steadily upon raising air saturation. An intracellular gluconic acid concentration of about 159 g/L (0.83 mol) was determined at 31 degrees C. This is to be compared with an extracellular concentration of 223 g/L (1.16 mol), which indicates the possible existence of an active transport system for gluconic acid secretion, or the presence of extracellular glucose oxidizing enzymes. The new process provides significant advantages over the traditional discontinuous fungi operations. The process control becomes easier, thus offering stable product quality and quantity.     

An integrated approach to optimization of Escherichia coli fermentations using historical data

Using a fermentation database for Escherichia coli producing green fluorescent protein (GFP), we have implemented a novel three-step optimization method to identify the process input variables most important in modeling the fermentation, as well as the values of those critical input variables that result in an increase in the desired output. In the first step of this algorithm, we use either decision-tree analysis (DTA) or information theoretic subset selection (ITSS) as a database mining technique to identify which process input variables best classify each of the process outputs (maximum cell concentration, maximum product concentration, and productivity) monitored in the experimental fermentations. The second step of the optimization method is to train an artificial neural network (ANN) model of the process input-output data, using the critical inputs identified in the first step. Finally, a hybrid genetic algorithm (hybrid GA), which includes both gradient and stochastic search methods, is used to identify the maximum output modeled by the ANN and the values of the input conditions that result in that maximum. The results of the database mining techniques are compared, both in terms of the inputs selected and the subsequent ANN performance. For the E. coli process used in this study, we identified 6 inputs from the original 13 that resulted in an ANN that best modeled the GFP fluorescence outputs of an independent test set. Values of the six inputs that resulted in a modeled maximum fluorescence were identified by applying a hybrid GA to the ANN model developed. When these conditions were tested in laboratory fermentors, an actual maximum fluorescence of 2.16E6 AU was obtained. The previous high value of fluorescence that was observed was 1.51E6 AU. Thus, this input condition set that was suggested by implementing the proposed optimization scheme on the available historical database increased the maximum fluorescence by 55%.     

Biosensor online control of citric acid production from glucose by Yarrowia lipolytica using semicontinuous fermentation

Our study aimed at the development of an effective method for citric acid production from glucose by use of the yeast Yarrowia lipolytica. The new method included an automated bioprocess control using a glucose biosensor. Several fermentation methodologies including batch, fed‐batch, repeated batch and repeated fed‐batch cultivation were tested. The best results were achieved during repeated fed‐batch cultivation: Within 3 days of cycle duration, approximately 100 g/L citric acid were produced. The yields reached values between 0.51 and 0.65 g/g and the selectivity of the bioprocess for citric acid was as high as 94%. Due to the elongation of the production phase of the bioprocess with growth‐decoupled citric acid production, and by operating the fermentation in cycles, an increase in citric acid production of 32% was achieved compared with simple batch fermentation.     

A hybrid of back propagation neural network and genetic algorithm for optimization of collagen extraction from Malaysian cultured catfish (Hybrid Clarias sp.)

Fractional factorial design (FFD) was applied to evaluate the effects of various process parameters in influencing the extraction efficiency of pepsin soluble collagen (PSC) from muscles of cultured catfish (Clarias gariepinus×C. macrocephalus). Result of the first order factorial design showed that acetic acid concentration, acid extraction time, acetic acid to muscles ratio, and stirring speed posed significant effect (P<0.05) on the yield of PSC obtained at the end of the extraction process. Two different artificial intelligence techniques namely artificial neural network (ANN) and genetic algorithm (GA) were then integrated for optimizing the extraction conditions to obtain the highest yield of PSC. The ANN was trained using the back propagation algorithm. A model was successfully generated with R ² value of 0.9527 and MSE value of 0.1672 for unseen data set, implying a good generalization of the network. Input parameters of the established ANN model were subsequently optimized using GA. The hybrid of ANN-GA model predicted a maximum extraction yield of PSC at 238.25 mg/g under the following conditions: an acetic acid concentration of 0.70 M, the acetic acid to muscles ratio of 25.78 mL/g, and the stirring speed of 432.50 rpm. Verification of the optimization showed the percentage error differences between the experimental and predicted values were less than 5%, indicating excellent modeling, predicting ability and optimization by the ANN-GA model.     

Modelling and off line optimization of batch gluconic acid fermentation

Summary The role of mathematical modelling and off line optimization for a batch fermentation process is described. The fermentation of gluconic acid by Acetobacter suboxydans ATCC 621 was studied. The model is based on a series of batch experiments in which the temperature was the only variable. The differential equations of the models were derived from these experiments to give the kinetic parameters and the parametric models varying with the temperature. The fermentation was optimized using Pontryagin's maximum principle. This gave the temperature profile of fermentation.Abbreviations x, g, l, S, c, P The concentration of cell mass, glucose, lactone, gluconic acid, 5-ketogluconic acid and total acidic products respectively - r₁, r₂ E₁ or E₂ enzyme in complex/total E₁ or E₂ enzyme content - a, b E₁ and E₂ enzyme content of unit quantity of biomass - k_i and K_j Rate constants - _max Maximum specific growth rate - y_x z₁=r₁x z₂=r₂x Yield coefficient of biomass with respect to growth on glucose - z₁ r₁x - z₂ r₂x     

Hybrid modeling in bioprocess dynamics: Structural variabilities,implementation strategies,and practical challenges

Hybrid modeling, with an appropriate blend of the mechanistic and data-driven framework, is increasingly being adopted in bioprocess modeling, model-based experimental design (digital-twin), identification of critical process parameters, and optimization. However, the development of a hybrid model from experimental data is an inherently complex workflow, involving designed experiments, selection of the data-driven process, identification of model parameters, assessment fitness, and generalization capability. Depending on the complexity of the process system and purpose, each piece of these modules can flexibly be incorporated into the puzzle. However, this extra flexibility can be a cause of concern to trace an “optimal” model structure. In this paper, the development of hybrid models in a common bioprocess system, selection of data-driven components and their mapping to states, choice of parameter identification techniques, and model quality assurance are revisited. The challenges associated with hybrid-model development, and corrective actions have also been reviewed. The review also suggests the lack of data, and code sharing in communal repositories can be a hurdle in the exploration, and expansion of those tools in a bioprocess system.     

An artificial intelligence tool for bioprocess monitoring: application to continuous production of gluconic acid by immobilized Aspergillus niger

Experimental data on continuous fermentation of sucrose and glucose solution at low pH to gluconic acid by Asprgillus niger immobilized on cellulose fabric show complex dynamic behaviour including a decline in yield. The data have been analyzed using an artificial intelligence based symbolic regression technique to provide a mathematical model for predicting values of conversion 5, 10 and 15 h ahead values of conversion. These predictions can be used during continuous operations to monitor the bioprocess and adjust the residence time of fermentation to get complete and more efficient conversion of sucrose or glucose to gluconic acid.     

Production of free gluconic acid by cells ofGluconobacter oxydans

Summary Microbial oxidation of glucose to free gluconic acid by growingG.oxydans batch cultures was investigated. Kinetic data for the discussed process were obtained and attention was paid mainly to the influence of the initial glucose concentration and of the conversion degree on the course of the process. It was determined that relatively high maximum specific growth rates of about 0,39 h⁻¹ and gluconic acid volumetric productivities up to 53 mmol/h could be reached usingG.oxydans NBIMCC 1043 in runs without pH control. A maximum conversion degree of 90,4% was achieved.     

Optimization of culture conditions for the production of Pleuromutilin from Pleurotus Mutilus using a hybrid method based on central composite design, neural network, and particle swarm optimization

This study aims at optimizing the culture conditions (agitation speed, temperature and pH) of the Pleuromutilin production by Pleurotus mutilus. A hybrid methodology including a central composite design (CCD), an artificial neural network (ANN), and a particle swarm optimization algorithm (PSO) was used. Specifically, the CCD and ANN were used for conducting experiments and modeling the non-linear process, respectively. The PSO was used for two purposes: Replacing the standard back propagation in training the ANN (PSONN) and optimizing the process. In comparison to the response surface methodology (RSM) and to the Bayesian regularization neural network (BRNN), PSONN model has shown the highest modeling ability. Under this hybrid approach (PSONN-PSO), the optimum levels of culture conditions were: 242 rpm agitation speed; temperature 26.88 and pH 6.06. A production of 10,074 ± 500 ??g/g, which was in very good agreement with the prediction (10,149 ??g/g), was observed in verification experiment. The hybrid PSONN-PSO gave a yield of 27.5% greater than that obtained by the hybrid BRNN-PSO. This work shows that the combination of PSONN with the generic PSO algorithm has a good predictability and a good accuracy for bio-process optimization. This hybrid approach is sufficiently general and thus can be helpful for modeling and optimization of other industrial bio-processes.     

A reinforcement learning-based hybrid modeling framework for bioprocess kinetics identification

Constructing predictive models to simulate complex bioprocess dynamics, particularly time-varying (i.e., parameters varying over time) and history-dependent (i.e., current kinetics dependent on historical culture conditions) behavior, has been a longstanding research challenge. Current advances in hybrid modeling offer a solution to this by integrating kinetic models with data-driven techniques. This article proposes a novel two-step framework: first (i) speculate and combine several possible kinetic model structures sourced from process and phenomenological knowledge, then (ii) identify the most likely kinetic model structure and its parameter values using model-free Reinforcement Learning (RL). Specifically, Step 1 collates feasible history-dependent model structures, then Step 2 uses RL to simultaneously identify the correct model structure and the time-varying parameter trajectories. To demonstrate the performance of this framework, a range of in-silico case studies were carried out. The results show that the proposed framework can efficiently construct high-fidelity models to quantify both time-varying and history-dependent kinetic behaviors while minimizing the risks of over-parametrization and over-fitting. Finally, the primary advantages of the proposed framework and its limitation were thoroughly discussed in comparison to other existing hybrid modeling and model structure identification techniques, highlighting the potential of this framework for general bioprocess modeling.     

Production of gluconic acid from glucose by Aspergillus niger: growth and non-growth conditions

Batch fermentation of glucose to gluconic acid was conducted using Aspergillus niger under growth and non-growth conditions using pure oxygen and air as a source of oxygen for the fermentation in 2 and 5 l stirred tank reactors (batch reactor). Production of gluconic acid under growth conditions was conducted in a 5 l batch reactor. Production and growth rates were higher during the period of supplying pure oxygen than that during supplying air, and the substrate consumption rate was almost constant. For the production of gluconic acid under non-growth conditions, conducted in the 2 l batch reactor, the effect of the pure oxygen flow rate and the biomass concentration on the gluconic acid production was investigated and an empirical equation suggested to show the dependence of the production rate r_p on the biomass concentration C_x and oxygen flow rate Q, at constant operating conditions (30 °C, 300 rpm and pH 5.5). Biomass concentration had a positive effect on the production rate r_p, and the effect of Q on r_p was positive at high biomass concentrations.     

Comparison of Modeling Methods for DoE‐Based Holistic Upstream Process Characterization

Upstream bioprocess characterization and optimization are time and resource‐intensive tasks. Regularly in the biopharmaceutical industry, statistical design of experiments (DoE) in combination with response surface models (RSMs) are used, neglecting the process trajectories and dynamics. Generating process understanding with time‐resolved, dynamic process models allows to understand the impact of temporal deviations, production dynamics, and provides a better understanding of the process variations that stem from the biological subsystem. The authors propose to use DoE studies in combination with hybrid modeling for process characterization. This approach is showcased on Escherichia coli fed‐batch cultivations at the 20L scale, evaluating the impact of three critical process parameters. The performance of a hybrid model is compared to a pure data‐driven model and the widely adopted RSM of the process endpoints. Further, the performance of the time‐resolved models to simultaneously predict biomass and titer is evaluated. The superior behavior of the hybrid model compared to the pure black‐box approaches for process characterization is presented. The evaluation considers important criteria, such as the prediction accuracy of the biomass and titer endpoints as well as the time‐resolved trajectories. This showcases the high potential of hybrid models for soft‐sensing and model predictive control.     

Intelligent real-time performance monitoring and quality prediction for batch/fed-batch cultivations

Supervision of batch bioprocess operations in real-time during the progress of a batch run offers many advantages over end-of-batch quality control. Multivariate statistical techniques such as multiway partial least squares (MPLS) provide an efficient modeling and supervision framework. A new type of MPLS modeling technique that is especially suitable for online real-time process monitoring and the multivariate monitoring charts are presented. This online process monitoring technique is also extended to include predictions of end-of-batch quality measurements during the progress of a batch run. Process monitoring, quality estimation and fault diagnosis activities are automated and supervised by embedding them into a real-time knowledge-based system (RTKBS). Interpretation of multivariate charts is also automated through a generic rule-base for efficient alarm handling. The integrated RTKBS and the implementation of MPLS-based process monitoring and quality control are illustrated using a fed-batch penicillin production benchmark process simulator.     

A multikinetic model approach to predict gluconic acid production in an airlift bioreactor

This paper uses a multikinetic approach to predict gluconic acid (GA) production performance in a 4.5 L airlift bioreactor (ALBR). The mathematical model consists of a set of simultaneous firstorder ordinary differential equations obtained from material balances of cell biomass, GA, glucose, and dissolved oxygen. Multikinetic models, namely, logistic and contois equations constitute kinetic part of the main model. The main model also takes into account the hydrodynamic and mass transfer parameters. These equations were solved using ODE solver of MATLAB v6.5 software. The mathematical model was validated with the experimental data available in the literature and is used to predict the effect of change in initial biomass and air sparging rate on the GA production. It is concluded that the mathematical model incorporated with multikinetic approach would be more efficient to predict the change in operating parameters on overall bioprocess of GA production in an ALBR.     

Modeling and design optimization of asynchronous flexible assembly systems with statistical process control and repair

This article presents the implementation of hybrid procedures involving the use of analytical performance evaluation techniques, discrete event simulation, and Monte Carlo optimization methods for the stochastic design optimization of asynchronous flexible assembly systems (AFASs) with statistical process control (SPC) and repair loops. AFASs are extremely complex and difficult to analyze in that such systems are subject to starvation and blocking effects, random jam occurrences at workstations, and splitting and merging of the assembly flow due to repair loops. Hence, an integrated approach simultaneously analyzing the interactions between product quality and optimal/near optimal system design is pursued. In the analytical analysis stage, a model based on GI/G/1 queueing network theory is used. In the Monte Carlo optimization stage, two alternative stochastic optimization approaches, namely, heuristic versions of stochastic quasigradient and simulated annealing algorithms, are implemented and compared in terms of their capabilities of solving complex AFAS design problems. The hybrid procedures presented appear to perform reasonably well in designing AFASs to reach a target production rate.     

A model‐driven approach towards rational microbial bioprocess optimization

Due to sustainability concerns, bio‐based production capitalizing on microbes as cell factories is in demand to synthesize valuable products. Nevertheless, the nonhomogenous variations of the extracellular environment in bioprocesses often challenge the biomass growth and the bioproduction yield. To enable a more rational bioprocess optimization, we have established a model‐driven approach that systematically integrates experiments with modeling, executed from flask to bioreactor scale, and using ferulic acid to vanillin bioconversion as a case study. The impacts of mass transfer and aeration on the biomass growth and bioproduction performances were examined using minimal small‐scale experiments. An integrated model coupling the cell factory kinetics with the three‐dimensional computational hydrodynamics of bioreactor was developed to better capture the spatiotemporal distributions of bioproduction. Full‐factorial predictions were then performed to identify the desired operating conditions. A bioconversion yield of 94% was achieved, which is one of the highest for recombinant Escherichia coli using ferulic acid as the precursor.     

Application of simplex‐based experimental optimization to challenging bioprocess development problems: Case studies in downstream processing

The identification of feasible operating conditions during the early stages of bioprocess development is implemented frequently through High Throughput (HT) studies. These typically employ techniques based on regression analysis, such as Design of Experiments. In this work, an alternative approach, based on a previously developed variant of the Simplex algorithm, is compared to the conventional regression‐based method for three experimental systems involving polishing chromatography and protein refolding. This Simplex algorithm variant was found to be more effective in identifying superior operating conditions, and in fact it reached the global optimum in most cases involving multiple optima. By contrast, the regression‐based method often failed to reach the global optimum, and in many cases reached poor operating conditions. The Simplex‐based method is further shown to be robust in dealing with noisy experimental data, and requires fewer experiments than regression‐based methods to reach favorable operating conditions. The Simplex‐variant also lends itself to the use of HT analytical methods, when they are available, which can assist in avoiding analytical bottlenecks. It is suggested that this Simplex‐variant is ideally suited to rapid optimization in early‐phase process development. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:404–419, 2016     

Optimization of batch and fed-batch bioreactors using simulated annealing

In order for biobased industrial products to compete economically with petroleum-derived products, significant reduction in their processing cost is necessary. Since most bioprocesses are operated in batch or fed-batch mode, their optimization involves theoretical and computational challenges. Simulated annealing (SA), a stochastic optimization algorithm, is used in this study to solve a number of challenging optimization problems related to the design and operation of bioreactors. Two well-known case studies are considered in which the robustness and efficiency of the SA algorithm is demonstrated. More specifically, in the first case study it is shown that the global optimal solution located by SA achieves significant improved productivity when compared with the results of previous investigations. In the second case study a realistic objective function is considered where the economic performance of a bioprocess is optimized. SA exhibits impeccable performance and robustness and was able to locate the global optimal solution irrespective of the initial point selected.