Dongda Zhang

Dynamic modelling of high biomass density cultivation and biohydrogen production in different scales of flat plate photobioreactors

This paper investigates the scaling‐up of cyanobacterial biomass cultivation and biohydrogen production from laboratory to industrial scale. Two main aspects are investigated and presented, which to the best of our knowledge have never been addressed, namely the construction of an accurate dynamic model to simulate cyanobacterial photo‐heterotrophic growth and biohydrogen production and the prediction of the maximum biomass and hydrogen production in different scales of photobioreactors. To achieve the current goals, experimental data obtained from a laboratory experimental setup are fitted by a dynamic model. Based on the current model, two key original findings are made in this work. First, it is found that selecting low‐chlorophyll mutants is an efficient way to increase both biomass concentration and hydrogen production particularly in a large scale photobioreactor. Second, the current work proposes that the width of industrial scale photobioreactors should not exceed 0.20 m for biomass cultivation and 0.05 m for biohydrogen production, as severe light attenuation can be induced in the reactor beyond this threshold. Biotechnol. Bioeng. 2015;112: 2429–2438.

Analysis of green algal growth via dynamic model simulation and process optimization

Chlamydomonas reinhardtii is a green microalga with the potential to generate sustainable biofuels for the future. Process simulation models are required to predict the impact of laboratory‐scale growth experiments on future scaled‐up system operation. Two dynamic models were constructed to simulate C. reinhardtii photo‐autotrophic and photo‐mixotrophic growth. A novel parameter estimation methodology was applied to determine the values of key parameters in both models, which were then verified using experimental results. The photo‐mixotrophic model was used to accurately predict C. reinhardtii growth under different light intensities and in different photobioreactor configurations. The optimal dissolved CO2 concentration for C. reinhardtii photo‐autotrophic growth was determined to be 0.0643 g·L−1, and the optimal light intensity for algal growth was 47 W·m−2. Sensitivity analysis revealed that the primary factor limiting C. reinhardtii growth was its intrinsic cell decay rate rather than light attenuation, regardless of the growth mode. The photo‐mixotrophic growth model was also applied to predict the maximum biomass concentration at different flat‐plate photobioreactors scales. A double‐exposure‐surface photobioreactor with a lower light intensity (less than 50 W·m−2) was the best configuration for scaled‐up C. reinhardtii cultivation. Three different short‐term (30‐day) C. reinhardtii photo‐mixotrophic cultivation processes were simulated and optimised. The maximum biomass productivity was 0.053 g·L−1·hr−1, achieved under continuous photobioreactor operation. The continuous stirred‐tank reactor was the best operating mode, as it provides both the highest biomass productivity and lowest electricity cost of pump operation. Biotechnol. Bioeng. 2015;112: 2025–2039.

Overproduction of L-tryptophan via simultaneous feed of glucose and anthranilic acid from recombinant Escherichia coli W3110: Kinetic modeling and process scale-up

L‐tryptophan is an essential amino acid widely used in food and pharmaceutical industries. However, its production via Escherichia coli fermentation suffers severely from both low glucose conversion efficiency and acetic acid inhibition, and to date effective process control methods have rarely been explored to facilitate its industrial scale production. To resolve these challenges, in the current research an engineered strain of E. coli was used to overproduce L‐tryptophan. To achieve this, a novel dynamic control strategy which incorporates an optimized anthranilic acid feeding into a dissolved oxygen‐stat (DO‐stat) glucose feeding framework was proposed for the first time. Three original contributions were observed. Firstly, compared to previous DO control methods, the current strategy was able to inhibit completely the production of acetic acid, and its glucose to L‐tryptophan yield reached 0.211 g/g, 62.3% higher than the previously reported. Secondly, a rigorous kinetic model was constructed to simulate the underlying biochemical process and identify the effect of anthranilic acid on both glucose conversion and L‐tryptophan synthesis. Finally, a thorough investigation was conducted to testify the capability of both the kinetic model and the novel control strategy for process scale‐up. It was found that the model possesses great predictive power, and the presented strategy achieved the highest glucose to L‐tryptophan yield (0.224 g/g) ever reported in large scale processes, which approaches the theoretical maximum yield of 0.227 g/g. This research, therefore, paves the way to significantly enhance the profitability of the investigated bioprocess.

An efficient model construction strategy to simulate microalgal lutein photo-production dynamic process†

Lutein is a high‐value bioproduct synthesized by microalga Desmodesmus sp. It has great potential for the food, cosmetics, and pharmaceutical industries. However, in order to enhance its productivity and to fulfil its ever‐increasing global market demand, it is vital to construct accurate models capable of simulating the entire behavior of the complicated dynamics of the underlying biosystem. To this aim, in this study two highly robust artificial neural networks (ANNs) are designed for the first time. Contrary to conventional ANNs, these networks model the rate of change of the dynamic system, which makes them highly relevant in practice. Different strategies are incorporated into the current research to guarantee the accuracy of the constructed models, which include determining the optimal network structure through a hyper‐parameter selection framework, generating significant amounts of artificial data sets by embedding random noise of appropriate size, and rescaling model inputs through standardization. Based on experimental verification, the high accuracy and great predictive power of the current models for long‐term dynamic bioprocess simulation in both real‐time and offline frameworks are thoroughly demonstrated. This research, therefore, paves the way to significantly facilitate the future investigation of lutein bioproduction process control and optimization. In addition, the model construction strategy developed in this research has great potential to be directly applied to other bioprocesses. Biotechnol. Bioeng. 2017;114: 2518–2527.

Dynamic modeling of green algae cultivation in a photobioreactor for sustainable biodiesel production

Biodiesel produced from microalgae has been extensively studied due to its potentially outstanding advantages over traditional transportation fuels. In order to facilitate its industrialization and improve the process profitability, it is vital to construct highly accurate models capable of predicting the complex behavior of the investigated biosystem for process optimization and control, which forms the current research goal. Three original contributions are described in this paper. Firstly, a dynamic model is constructed to simulate the complicated effect of light intensity, nutrient supply and light attenuation on both biomass growth and biolipid production. Secondly, chlorophyll fluorescence, an instantly measurable variable and indicator of photosynthetic activity, is embedded into the model to monitor and update model accuracy especially for the purpose of future process optimal control, and its correlation between intracellular nitrogen content is quantified, which to the best of our knowledge has never been addressed so far. Thirdly, a thorough experimental verification is conducted under different scenarios including both continuous illumination and light/dark cycle conditions to testify the model predictive capability particularly for long‐term operation, and it is concluded that the current model is characterized by a high level of predictive capability. Based on the model, the optimal light intensity for algal biomass growth and lipid synthesis is estimated. This work, therefore, paves the way to forward future process design and real‐time optimization.

A Bilevel Programming Approach for the Dynamic Optimization of Cyanobacterial C-phycocyanin Production under Uncertainty

Abstract C-phycocyanin is a high-value bioproduct synthesized by cyanobacterium Arthrospira platensis with a significant global market demand given its applications in the pharmaceutical, food and colorant industries. Unfortunately, its biosynthesis is currently characterized by low productivity and large uncertainty during the production process. High variability and unreliable expectations on product yields substantially hinder the industrialization of microorganism derived biochemicals as they present a risk to the profitability and safety of the underlying systems. Therefore, in this work, we propose a robust optimization approach to determine the lower and upper product yield expectations for the sustainable production of C-phycocyanin. Kinetic modeling is adopted in this study as a tool for fast prototyping, prediction and optimization of chemical and biochemical processes. On the upside, parameters in bioprocess kinetic models are used as a simplification of the complex metabolic networks to enable the simulation, design and control of the process. On the downside, this conglomeration of parameters may result in significant model uncertainty. To address this challenge, we formulate a bilevel max-min optimization problem to obtain the worst-case scenario of our system given the uncertainty on the model parameters. By constructing parameter confidence ellipsoids, we determined the feasible region along which the parameters can minimize the system’s performance, while nutrient and light controls are used to maximize the biorenewable production. The inner minimization problem is embedded by means of the optimality conditions into the upper maximization problem and hence both are solved simultaneously. Through this approach, we determined pessimistic and optimistic scenarios for the bioproduction of C-phycocyanin and hence compute reliable expectations on the yield and profit of the process.