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

Strategies to enhance biohydrogen production from microalgae: A comprehensive review

Microalgae represent a promising renewable feedstock for the sustainable production of biohydrogen. Their high growth rates and ability to fix carbon utilizing just sunlight, water, and nutrients make them well-suited for this application. Recent advancements have focused on improving microalgal hydrogen yields and cultivation methods. This review aims to summarize recent developments in microalgal cultivation techniques and genetic engineering strategies for enhanced biohydrogen production. Specific areas of focus include novel microalgal species selection, immobilization methods, integrated hybrid systems, and metabolic engineering. Studies related to microalgal strain selection, cultivation methods, metabolic engineering, and genetic manipulations were compiled and analyzed. Promising microalgal species with high hydrogen production capabilities such as Synechocystis sp., Anabaena variabilis, and Chlamydomonas reinhardtii have been identified. Immobilization techniques like encapsulation in alginate and integration with dark fermentation have led to improved hydrogen yields. Metabolic engineering through modulation of hydrogenase activity and photosynthetic pathways shows potential for enhanced biohydrogen productivity. Considerable progress has been made in developing microalgal systems for biohydrogen. However, challenges around process optimization and scale-up remain. Future work involving metabolic modeling, photobioreactor design, and genetic engineering of electron transfer pathways could help realize the full potential of this renewable technology.

Using different cultivation strategies and methods for the production of microalgal biomass as a raw material for the generation of bioproducts

Microalgal biomass and its fine chemical production from microalgae have pioneered algal bioprocess technology with few limitations such as lab-to-industry. However, laboratory-scale transitions and industrial applications are hindered by a plethora of limitations comprising expensive in culturing methods. Therefore, to emphasize the profitable benefits, the algal culturing techniques appropriately employed for large-scale microalgal biomass yield necessitates intricate assessment to emphasize the profitable benefits. The present review holistically compiles the culturing strategies for improving microalgal biomass production based on appropriate factors like designing better bioreactor designs. On the other hand, synthetic biology approaches for abridging the effective industrial transition success explored recently. Prospects in synthetic biology for enhanced microalgal biomass production based on cultivation strategies and various mechanistic modes approach to enrich cost-effective and viable output are discussed. The State-of-the-art culturing techniques encompassing enhancement of photosynthetic activity, designing bioreactor design, and potential augmenting protocols for biomass yield employing indoor cultivation in both (Open and or/closed) methods are enumerated. Further, limitations hindering the microalgal bioproducts development are critically evaluated for improving culturing techniques for microalgal cell factories, subsequently escalating the cost-benefit ratio in bioproducts synthesis from microalgae. The comprehensive analysis could provide a rational and deeper detailed insight for microalgal entrepreneurs through alternative culturing technology viz., synthetic biology and genome engineering in an Industrial perspective arena.

Kinetic and hybrid modeling for yeast astaxanthin production under uncertainty

Astaxanthin is a high-value compound commercially synthesized through Xanthophyllomyces dendrorhous fermentation. Using mixed sugars decomposed from biowastes for yeast fermentation provides a promising option to improve process sustainability. However, little effort has been made to investigate the effects of multiple sugars on X. dendrorhous biomass growth and astaxanthin production. Furthermore, the construction of a high-fidelity model is challenging due to the system's variability, also known as batch-to-batch variation. Two innovations are proposed in this study to address these challenges. First, a kinetic model was developed to compare process kinetics between the single sugar (glucose) based and the mixed sugar (glucose and sucrose) based fermentation methods. Then, the kinetic model parameters were modeled themselves as Gaussian processes, a probabilistic machine learning technique, to improve the accuracy and robustness of model predictions. We conclude that although the presence of sucrose does not affect the biomass growth kinetics, it introduces a competitive inhibitory mechanism that enhances astaxanthin accumulation by inducing adverse environmental conditions such as osmotic gradients. Moreover, the hybrid model was able to greatly reduce model simulation error and was particularly robust to uncertainty propagation. This study suggests the advantage of mixed sugar-based fermentation and provides a novel approach for bioprocess dynamic modeling.

Synergisms of machine learning and constraint-based modeling of metabolism for analysis and optimization of fermentation parameters

Recent noteworthy advances in the development of high-performing microbial and mammalian strains have enabled the sustainable production of bio-economically valuable substances such as bio-compounds, biofuels, and biopharmaceuticals. However, to obtain an industrially viable mass-production scheme, much time and effort are required. The robust and rational design of fermentation processes requires analysis and optimization of different extracellular conditions and medium components, which have a massive effect on growth and productivity. In this regard, knowledge- and data-driven modeling methods have received much attention. Constraint-based modeling (CBM) is a knowledge-driven mathematical approach that has been widely used in fermentation analysis and optimization due to its capabilities of predicting the cellular phenotype from genotype through high-throughput means. On the other hand, machine learning (ML) is a data-driven statistical method that identifies the data patterns within sophisticated biological systems and processes, where there is inadequate knowledge to represent underlying mechanisms. Furthermore, ML models are becoming a viable complement to constraint-based models in a reciprocal manner when one is used as a pre-step of another. As a result, more predictable model is produced. This review highlights the applications of CBM and ML independently and the combination of these two approaches for analyzing and optimizing fermentation parameters. This article is protected by copyright. All rights reserved.

H2 production by photofermentation in an innovative plate-type photobioreactor with meandering channels

Hydrogen production by Rhodobacter capsulatus is an anaerobic, photobiological process requiring specific mixing conditions. In this study, an innovative design of a photobioreactor is proposed. The design is based on a plate-type photobioreactor with an interconnected meandering channel to allow culture mixing and H₂ degassing. The culture flow was characterized as a quasi-plug-flow with radial mixing caused by a turbulent-like regime achieved at a low Reynolds number. The dissipated volumetric power was decreased 10-fold while maintaining PBR performances (production and yields) when compared with a magnetically stirred tank reactor. To increase hydrogen production flow rate, several bacterial concentrations were tested by increasing the glutamate concentration using fed-batch cultures. The maximum hydrogen production flow rate (157.7 ± 9.3 ml H₂ /L/h) achieved is one of the highest values so far reported for H₂ production by R. capsulatus. These first results are encouraging for future scale-up of the plate-type reactor.

Systems biology approaches integrated with artificial intelligence for optimized metabolic engineering

Metabolic engineering aims to maximize the production of bio-economically important substances (compounds, enzymes, or other proteins) through the optimization of the genetics, cellular processes and growth conditions of microorganisms. This requires detailed understanding of underlying metabolic pathways involved in the production of the targeted substances, and how the cellular processes or growth conditions are regulated by the engineering. To achieve this goal, a large system of experimental techniques, compound libraries, computational methods and data resources, including multi-omics data, are used. The recent advent of multi-omics systems biology approaches significantly impacted the field by opening new avenues to perform dynamic and large-scale analyses that deepen our knowledge on the manipulations. However, with the enormous transcriptomics, proteomics and metabolomics available, it is a daunting task to integrate the data for a more holistic understanding. Novel data mining and analytics approaches, including Artificial Intelligence (AI), can provide breakthroughs where traditional low-throughput experiment-alone methods cannot easily achieve. Here, we review the latest attempts of combining systems biology and AI in metabolic engineering research, and highlight how this alliance can help overcome the current challenges facing industrial biotechnology, especially for food-related substances and compounds using microorganisms.

CO2 to succinic acid - Estimating the potential of biocatalytic routes

Microbial carbon dioxide assimilation and conversion to chemical platform molecules has the potential to be developed as economic, sustainable processes. The carbon dioxide assimilation can proceed by a variety of natural pathways and recently even synthetic CO2 fixation routes have been designed. Early assessment of the performance of the different carbon fixation alternatives within biotechnological processes is desirable to evaluate their potential. Here we applied stoichiometric metabolic modeling based on physiological and process data to evaluate different process variants for the conversion of C1 carbon compounds to the industrial relevant platform chemical succinic acid. We computationally analyzed the performance of cyanobacteria, acetogens, methylotrophs, and synthetic CO2 fixation pathways in Saccharomyces cerevisiae in terms of production rates, product yields, and the optimization potential. This analysis provided insight into the economic feasibility and allowed to estimate the future industrial applicability by estimating overall production costs. With reported, or estimated data of engineered or wild type strains, none of the simulated microbial succinate production processes showed a performance allowing competitive production. The main limiting factors were identified as gas and photon transfer and metabolic activities whereas metabolic network structure was not restricting. In simulations with optimized parameters most process alternatives reached economically interesting values, hence, represent promising alternatives to sugar-based fermentations.

A novel mechanistic model for nitrogen removal in algal-bacterial photo sequencing batch reactors

A comprehensive mathematical model was constructed to evaluate the complex substrate and microbial interaction in algal-bacterial photo sequencing batch reactors (PSBR). The kinetics of metabolite, growth and endogenous respiration of ammonia oxidizing bacteria, nitrite oxidizing bacteria and heterotrophic bacteria were coupled to those of microalgae and then embedded into widely-used activated sludge model series. The impact of light intensity was considered for microalgae growth, while the effect of inorganic carbon was considered for each microorganism. The integrated model framework was assessed using experimental data from algal-bacterial consortia performing sidestream nitritation/denitritation. The validity of the model was further evaluated based on dataset from PSBR performing mainstream nitrification. The developed model could satisfactorily capture the dynamics of microbial populations and substrates under different operational conditions (i.e. feeding, carbon dosing and illuminating mode, light intensity, influent ammonium concentration), which might serve as a powerful tool for optimizing the novel algal-bacterial nitrogen removal processes.

Microalgal cultivation and hydrodynamic characterization using a novel tubular photobioreactor with helical blade rotors

Industrial-scale microalgal cultivation for food, feedstocks and biofuel production is limited by several engineering factors, such as cultivation systems. As a closed microalgal growth system, tubular photobioreactors are the most preferred ones in the mass algae production. In this work, microalgal cultivation and hydrodynamic characterization using a novel tubular photobioreactor equipped with helical blade rotors (HBRs) were investigated, with the aid of computational fluid dynamics and cultivation experiments to evaluate the effect of HBRs on the performance of tubular photobioreactor and growth of the Chlorella sp. The results showed that the use of HBRs in tubular photobioreactors would result in swirl flow and increase of radial velocity and circumferential velocity; it also indicated that the HBRs would enable microalgal cells to move forward helically and to be shuttled alternatively between the light zone and the dark zone. This has led to the faster growth rate of Chlorella sp. and no attachment on the tube surface in the tubular photobioreactor during the whole cultivation cycle. In conclusion, the HBRs could improve the performance of tubular photobioreactors and thus impact positively on the cultivation of microalgae cells for biotechnological industry.

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. © 2017 Wiley Periodicals, Inc.

Reaction engineering analysis of Scenedesmus ovalternus in a flat-plate gas-lift photobioreactor

Microalgal strains of the genus Scenedesmus are a promising resource for commercial biotechnological applications. The temperature-, pH- and light-dependent growth of Scenedesmus ovalternus has been investigated on a laboratory scale. Best batch process performance was obtained at 30°C, pH 8.0 and an incident photon flux density of 1300μmolphotonsm^-2s^-1 using a flat-plate gas-lift photobioreactor. Highest growth rate (0.11h^-1) and space-time yield (1.7±0.1g_CDWL^-1d^-1) were observed when applying these reaction conditions. Biomass concentrations of up to 7.5±0.1g_CDWL^-1 were achieved within six days (25.0±0.5g_CDWm^-2d^-1). The light-dependent growth kinetics of S. ovalternus was identified using Schuster's light transfer model and Andrews' light inhibition model (K_S=545μmolphotonsm^-2s^-1; K_I=2744μmolphotonsm^-2s^-1; μ_max=0.21h^-1). The optimal mean integral photon flux density for growth of S. ovalternus was estimated to be 1223μmolphotonsm^-2s^-1.