Optimal Feeding Trajectories Design for E. coli Fed-batch Fermentations

In this paper optimal control algorithms for two E. coli fed-batch fermentations are developed. Fed-batch fermentation processes of E. coli strain MC4110 and E. coli strain BL21(DE3)pPhyt109 are considered. Simple material balance models are used to describe the E. coli fermentation processes. The optimal feed rate control of a primary metabolite process is studied and a biomass production is used as an example. The optimization of the considered fed-batch fermentation processes is done using the calculus of variations to determine the optimal feed rate profiles. The problem is formulated as a free final time problem where the control objective is to maximize biomass at the end of the process. The obtained optimal feed rate profiles consist of sequences of maximum and minimum feed rates. The resulting profiles are used for optimization of the E. coli fed-batch fermentations. Presented simulations show a good efficiency of the developed optimal feed rate profiles

Prediction of the biogas production using GA and ACO input features selection method for ANN model

This paper presents a fast and reliable approach to analyze the biogas production process with respect to the biogas production rate. The experimental data used for the developed models included 15 process variables measured at an agricultural biogas plant in Germany. In this context, the concentration of volatile fatty acids, total solids, volatile solids acid detergent fibre, acid detergent lignin, neutral detergent fibre, ammonium nitrogen, hydraulic retention time, and organic loading rate were used. Artificial neural networks (ANN) were established to predict the biogas production rate. An ant colony optimization and genetic algorithms were implemented to perform the variable selection. They identified the significant process variables, reduced the model dimension and improved the prediction capacity of the ANN models. The best prediction of the biogas production rate was obtained with an error of prediction of 6.24% and a coefficient of determination of R2 = 0.9

Assessment of the energy and exergy efficiencies of farm to fork grain cultivation and bread making processes in Turkey and Germany

Energy and exergy efficiencies of the wheat and rye bread and hamburger bunmaking processes are assessed based on data from Turkey and Germany. Amount of the land required to produce the same amount of wheat in Turkey is 3.34 times of that required in Germany; this ratio is 2.30 for the rye grain. These results show that the efficiency of the conversion of the solar energy into the grain mass is low in Turkey. Cumulative degree of perfection (CDP) for the wheat and the rye grain production is 3.73 and 4.96 in Turkey, and 11.26 and 10.46 in Germany. Specific energy utilization for rye bread production is almost the same in Turkey and Germany; but it is 12 % higher in Turkey for wheat bread and hamburger bun making. Hamburger bun production requires the maximum energy utilization due to the higher weight loss in baking. The rye bread production process requires the minimum energy utilization due to the lower energy input in the agriculture and higher efficiency in the flour production. The maximum exergy destructions occur during the milling and the baking steps

Modelling of Functional States during Saccharomyces cerevisiae Fed-batch Cultivation

An implementation of functional state approach for modelling of yeast fed-batch cultivation is presented in this paper. Using of functional state modelling approach aims to overcome the main disadvantage of using global process model, namely complex model structure and big number of model parameters, which complicate the model simulation and parameter estimation. This approach has computational advantages, such as the possibility to use the estimated values from the previous state as starting values for estimation of parameters of a new state. The functional state modelling approach is applied here for fedbatch cultivation of Saccharomyces cerevisiae. Four functional states are recognised and parameter estimation of local models is presented as well

Implementation of Sliding Mode Controller with Boundary Layer for Saccharomyces cerevisiae Fed-batch Cultivation

An implementation of sliding mode control for yeast fed-batch cultivation is presented in this paper. Developed controller has been implemented on two real fed-batch cultivations of Saccharomyces cerevisiae. The controller successfully stabilizes the process and shows a very good performance at high input disturbances

Implementation of QbD strategies in the inoculum expansion of a mAb production process

The quality by design approach was introduced to the biopharmaceutical industry over 15 years ago. This principle is widely implemented in the characterization of monoclonal antibody production processes. Anyway, the early process phase, namely the inoculum expansion, was not yet investigated and characterized for most processes. In order to increase the understanding of early process parameter interactions and their influence on the later production process, a risk assessment followed by a design of experiments approach was conducted. The DoE included the critical parameters methotrexate (MTX) concentration, initial passage viable cell density and passage duration. Multivariate data analysis led to mathematical regression models and the establishment of a designated design space for the studied parameters. It was found that the passage duration as well as the initial viable cell density for each passage during the inoculum expansion have severe effects on the growth rate and viability of the early process phase. Furthermore, the variations during the inoculum expansion directly influenced the production process responses. This carry-over of factor effects highlights the crucial impact of early process failures and the importance of process analysis and control during the first part of mAb production processes. © 2020 The Authors. Engineering in Life Sciences published by Wiley-VCH Gmb

Multiple model approach to modelling of Escherichia coli fed-batch cultivation extracellular production of bacterial phytase

The paper presents the implementation of multiple model approach to modelling of Escherichia coli BL21(DE3)pPhyt109 fed-batch cultivation processes for an extracellular production of bacterial phytase. Due to the complex metabolic pathways of microorganisms, the accurate modelling of bioprocesses is rather difficult. Multiple model approach is an alternative concept which helps in modelling and control of complex processes. The main idea is the development of a model based on simple submodels for the purposes of further high quality process control. The presented simulations of E. coli fed-batch cultivation show how the process could be divided into different functional states and how the model parameters could be obtained easily using genetic algorithms. The obtained results and model verification demonstrate the effectiveness of the applied concept of multiple model approach and of the proposed identification scheme. © 2007 by Pontificia Universidad Católica de Valparaíso

Multiple model approach to modelling of Escherichia coli fed-batch cultivation extracellular production of bacterial phytase

The paper presents the implementation of multiple model approach to modelling of Escherichia coli BL21(DE3)pPhyt109 fed-batch cultivation processes for an extracellular production of bacterial phytase. Due to the complex metabolic pathways of microorganisms, the accurate modelling of bioprocesses is rather difficult. Multiple model approach is an alternative concept which helps in modelling and control of complex processes. The main idea is the development of a model based on simple submodels for the purposes of further high quality process control. The presented simulations of E. coli fed-batch cultivation show how the process could be divided into different functional states and how the model parameters could be obtained easily using genetic algorithms. The obtained results and model verification demonstrate the effectiveness of the applied concept of multiple model approach and of the proposed identification scheme

Special Issue: Bioprocess Monitoring and Control

Bioprocesses can be found in different areas such as the production of food, feed, energy, chemicals, and pharmaceuticals [...

Application of Nature-Inspired Multi-Objective Optimization Algorithms to Improve the Bakery Production Efficiency

This contribution investigates the performance of nature-inspired multi-objective optimization algorithms to reduce the makespan and oven idle time of bakery manufacturing using a hybrid no-wait flow shop scheduling model. As an example, the production data from a bakery with 40 products is investigated. We use the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) to determine the tradeoffs between the two objectives. The computational results reveal that the nature-inspired optimization algorithms provide solutions with a significant 8.7% reduction in makespan. Nonetheless, the algorithms provide solutions with a longer oven idle time to achieve the single goal of makespan minimization. This consequently elevates energy waste and production expenditure. The current study shows that an alternative Pareto optimal solution significantly reduces oven idle time while losing a marginal amount of makespan. Furthermore, the Pareto solution reduces oven idle time by 93 min by expanding the makespan by only 8 min. The proposed approach has the potential to be an influential tool for small- and medium-sized bakeries seeking economic growth and, as a result, gain in market competition