Modelling of multi-nutrient interactions in growth of the dinoflagellate microalga Protoceratium reticulatum using artificial neural networks

Advances in microalgae-based carbon sequestration: Current status and future perspectives

The advancement in carbon dioxide (CO2) sequestration technology has received significant attention due to the adverse effects of CO2 on climate. The mitigation of the adverse effects of CO2 can be accomplished through its conversion into useful products or renewable fuels. In this regard, microalgae is a promising candidate due to its high photosynthesis efficiency, sustainability, and eco-friendly nature. Microalgae utilizes CO2 in the process of photosynthesis and generates biomass that can be utilized to produce various valuable products such as supplements, chemicals, cosmetics, biofuels, and other value-added products. However, at present microalgae cultivation is still restricted to producing value-added products due to high cultivation costs and lower CO2 sequestration efficiency of algal strains. Therefore, it is very crucial to develop novel techniques that can be cost-effective and enhance microalgal carbon sequestration efficiency. The main aim of the present manuscript is to explain how to optimize microalgal CO2 sequestration, integrate valuable product generation, and explore novel techniques like genetic manipulations, phytohormones, quantum dots, and AI tools to enhance the efficiency of CO2 sequestration. Additionally, this review provides an overview of the mass flow of different microalgae and their biorefinery, life cycle assessment (LCA) for achieving net-zero CO2 emissions, and the advantages, challenges, and future perspectives of current technologies. All of the reviewed approaches efficiently enhance microalgal CO2 sequestration and integrate value-added compound production, creating a green and economically profitable process.

Artificial neural network and genetic algorithm coupled fermentation kinetics to regulate L-lysine fermentation

Fermentation plays a pivotal role in the industrialization of bioproducts, yet there is a substantial lag in the fermentation process regulation. Here, an artificial neural network (ANN) and genetic algorithm (GA) coupled with fermentation kinetics were employed to establish an innovative lysine fermentation control. Firstly, the strategy of coupling GA with ANN was established. Secondly, specific lysine formation rate (qp), specific substrate consumption rate (qs), and specific cell growth rate (μ) were predicted and optimized by ANN-GA. The optimal ANN model adopts a three-layer feed-forward back-propagation structure (4:10:1). The optimal fermentation control parameters are obtained through GA. Finally, when the carbon to nitrogen ratio, residual sugar concentration, ammonia nitrogen concentration, and dissolved oxygen were [2.5, 4.5], [6.5, 9.5] g·L-1, [1.0, 2.0] g·L-1 and [20, 30] %, respectively, the lysine concentration reaches its peak at 213.0 ± 5.10 g·L-1. The novel control strategy holds significant potential for optimizing the fermentation of other bioproducts.

Artificial intelligence and machine learning tools for high-performance microalgal wastewater treatment and algal biorefinery: A critical review

The increased water scarcity, depletion of freshwater resources, and rising environmental awareness are stressing for the development of sustainable wastewater treatment processes. Microalgae-based wastewater treatment has resulted in a paradigm shift in our approach toward nutrient removal and simultaneous resource recovery from wastewater. Wastewater treatment and the generation of biofuels and bioproducts from microalgae can be coupled to promote the circular economy synergistically. A microalgal biorefinery transforms microalgal biomass into biofuels, bioactive chemicals, and biomaterials. The large-scale cultivation of microalgae is essential for the commercialization and industrialization of microalgae biorefinery. However, the inherent complexity of microalgal cultivation parameters regarding physiological and illumination parameters renders it challenging to facilitate a smooth and cost-effective operation. Artificial intelligence (AI)/machine learning algorithms (MLA) offer innovative strategies for assessing, predicting, and regulating uncertainties in algal wastewater treatment and biorefinery. The current study presents a critical review of the most promising AI/MLAs that demonstrate a potential to be applied in microalgal technologies. The most commonly used MLAs include artificial neural networks, support vector machine, genetic algorithms, decision tree, and random forest algorithms. Recent developments in AI have made it possible to combine cutting-edge techniques from AI research fields with microalgae for accurate analysis of large datasets. MLAs have been extensively studied for their potential in microalgae detection and classification. However, the ML application in microalgal industries, such as optimizing microalgae cultivation for increased biomass productivity, is still in its infancy. Incorporating smart AI/ML-enabled Internet of Things (IoT) based technologies can help the microalgal industries to operate effectively with minimum resources. Future research directions are also highlighted, and some of the challenges and perspectives of AI/ML are outlined. As the world is entering the digitalized industrial era, this review provides an insightful discussion about intelligent microalgal wastewater treatment and biorefinery for researchers in the field of microalgae.

Microalgae with artificial intelligence: A digitalized perspective on genetics, systems and products

With recent advances in novel gene-editing tools such as RNAi, ZFNs, TALENs, and CRISPR-Cas9, the possibility of altering microalgae toward designed properties for various application is becoming a reality. Alteration of microalgae genomes can modify metabolic pathways to give elevated yields in lipids, biomass, and other components. The potential of such genetically optimized microalgae can give a "domino effect" in further providing optimization leverages down the supply chain, in aspects such as cultivation, processing, system design, process integration, and revolutionary products. However, the current level of understanding the functional information of various microalgae gene sequences is still primitive and insufficient as microalgae genome sequences are long and complex. From this perspective, this work proposes to link up this knowledge gap between microalgae genetic information and optimized bioproducts using Artificial Intelligence (AI). With the recent acceleration of AI research, large and complex data from microalgae research can be properly analyzed by combining the cutting-edge of both fields. In this work, the most suitable class of AI algorithms (such as active learning, semi-supervised learning, and meta-learning) are discussed for different cases of microalgae applications. This work concisely reviews the current state of the research milestones and highlight some of the state-of-art that has been carried out, providing insightful future pathways. The utilization of AI algorithms in microalgae cultivation, system optimization, and other aspects of the supply chain is also discussed. This work opens the pathway to a digitalized future for microalgae research and applications.

Pilot-scale outdoor photobioreactor culture of the marine dinoflagellate Karlodinium veneficum: Production of a karlotoxins-rich extract

A pilot-scale bioprocess was developed for the production of karlotoxin-enriched extracts of the marine algal dinoflagellate Karlodinium veneficum. A bubble column and a flat-panel photobioreactors (80-281 L) were used for comparative assessment of growth. Flow hydrodynamics and energy dissipation rates (EDR) in the bioreactors were characterized through robust computational fluid dynamic simulations. All cultures were conducted monoseptically outdoors. Bubble column (maximum cell productivity in semicontinuous operation of 58 × 10³ cell mL^-1 day^-1) proved to be a better culture system for this alga. In both reactors, the local EDR near the headspace, and in the sparger zone, were more than one order of magnitude higher than the average value in the whole reactor (=4 × 10^-3 W kg^-1). Extraction of the culture and further purification resulted in the desired KTXs extracts. Apparently, the alga produced three congeners KTXs: KmTx-10 and its sulfated derivative (sulfo-KmTx-10) and KmTx-12. All congeners possessed hemolytic activity.

Biotechnological and Pharmacological Applications of Biotoxins and Other Bioactive Molecules from Dinoflagellates

The long-lasting interest in bioactive molecules (namely toxins) produced by (microalga) dinoflagellates has risen in recent years. Exhibiting wide diversity and complexity, said compounds are well-recognized for their biological features, with great potential for use as pharmaceutical therapies and biological research probes. Unfortunately, provision of those compounds is still far from sufficient, especially in view of an increasing demand for preclinical testing. Despite the difficulties to establish dinoflagellate cultures and obtain reasonable productivities of such compounds, intensive research has permitted a number of advances in the field. This paper accordingly reviews the characteristics of some of the most important biotoxins (and other bioactive substances) produced by dinoflagellates. It also presents and discusses (to some length) the main advances pertaining to dinoflagellate production, from bench to large scale-with an emphasis on material published since the latest review available on the subject. Such advances encompass improvements in nutrient formulation and light supply as major operational conditions; they have permitted adaptation of classical designs, and aided the development of novel configurations for dinoflagellate growth-even though shearing-related issues remain a major challenge.

The use of an artificial neural network to model the infection strategy for baculovirus production in suspended insect cell cultures

Since the infection strategy in the baculovirus-insect cell system mostly affects production of the vector itself or the target product, and given that individual infection parameters interact with each other, the optimal combination must be established for each such specific system. In this work an artificial neural network was used to model infection strategy, including the cell concentration at infection, the multiplicity of infection, the medium recycle, and agitation intensity, and to evaluate the relative importance of each factor in the baculovirus production obtained. The results demonstrate that this model can be used to select an optimal infection strategy. For the baculovirus-insect cell system used in this study, this includes low multiplicity of infection and agitation intensity, along with high cell concentration at infection and medium recycle. Our model is superior to regression methods and predicts baculovirus production more precisely, thus meaning that it could be useful for the development of feasible processes, thereby improving process performance and economy.

Modeling and simulation of xylitol production in bioreactor by Debaryomyces nepalensis NCYC 3413 using unstructured and artificial neural network models

This study examines the use of unstructured kinetic model and artificial neural networks as predictive tools for xylitol production by Debaryomyces nepalensis NCYC 3413 in bioreactor. An unstructured kinetic model was proposed in order to assess the influence of pH (4, 5 and 6), temperature (25°C, 30°C and 35°C) and volumetric oxygen transfer coefficient kLa (0.14h(-1), 0.28h(-1) and 0.56h(-1)) on growth and xylitol production. A feed-forward back-propagation artificial neural network (ANN) has been developed to investigate the effect of process condition on xylitol production. ANN configuration of 6-10-3 layers was selected and trained with 339 experimental data points from bioreactor studies. Results showed that simulation and prediction accuracy of ANN was apparently higher when compared to unstructured mechanistic model under varying operational conditions. ANN was found to be an efficient data-driven tool to predict the optimal harvest time in xylitol production.

The optimization of the fermentation process of wheat germ for flavonoids and two benzoquinones using EKF-ANN and NSGA-II

Bi-objective optimization of wheat germ fermentation using EKF-ANN combined with NSGA-II.