Cultivation of Chlorella vulgaris on dairy waste using vision imaging for biomass growth monitoring

An artificial intelligence approach for identification of microalgae cultures

In this work, a model for the characterization of microalgae cultures based on artificial neural networks has been developed. The characterization of microalgae cultures is essential to guarantee the quality of the biomass, and the objective of this work is to achieve a simple and fast method to address this issue. Data acquisition was performed using FlowCam, a device capable of capturing images of the cells detected in a culture sample, which are used as inputs by the model. The model can distinguish between 6 different genera of microalgae, having been trained with several species of each genus. It was further complemented with a classification threshold to discard unwanted objects while improving the overall accuracy of the model. The model achieved an accuracy of up to 97.27% when classifying a culture. The results demonstrate the effectiveness of the Deep Learning models for the characterization of microalgae cultures, it being a useful tool for the monitoring of microalgae cultures in large-scale production facilities while providing accurate characterization over a wide range of genera.

V. An improved beluga whale optimizer-Derived Adaptive multi-channel DeepLabv3+ for semantic segmentation of aerial images

Semantic segmentation process over Remote Sensing images has been regarded as hot research work. Even though the Remote Sensing images provide many essential features, the sampled images are inconsistent in size. Even if a similar network can segment Remote Sensing images to some extents, segmentation accuracy needs to be improved. General neural networks are used to improve categorization accuracy, but they also caused significant losses to target scale and spatial features, and the traditional common features fusion techniques can only resolve some of the issues. A segmentation network has been designed to resolve the above-mentioned issues as well. With the motive of addressing the difficulties in the existing semantic segmentation techniques for aerial images, the adoption of deep learning techniques is utilized. This model has adopted a new Adaptive Multichannel Deeplabv3+ (AMC-Deeplabv3+) with the help of a new meta-heuristic algorithm called Improved Beluga whale optimization (IBWO). Here, the hyperparameters of Multichannel deeplabv3+ are optimized by the IBWO algorithm. The proposed model significantly enhances the performance of the overall system by measuring the accuracy and dice coefficient. The proposed model attains improved accuracies of 98.65% & 98.72% for dataset 1 and 2 respectively and also achieves the dice coefficient of 98.73% & 98.85% respectively with a computation time of 113.0123 seconds. The evolutional outcomes of the proposed model show significantly better than the state of the art techniques like CNN, MUnet and DFCNN models.

A comparative analysis of growth kinetics, image analysis, and biofuel potential of different algal strains

Due to the global population growth and economic development, energy demand has increased worldwide. Countries take steps to improve their alternative and renewable energy sources. Algae is one of the alternative energy sources and can be used to produce renewable biofuel. In this study, nondestructive, practical, and rapid image processing techniques were applied to determine the algal growth kinetics and biomass potential of four algal strains, including C. minutum, Chlorella sorokiniana, C. vulgaris, and S. obliquus. Laboratory experiments were conducted to determine different aspects of biomass and chlorophyll production of those algal strains. Suitable non-linear growth models, including Logistic, modified Logistic, Gompertz, and modified Gompertz models, were employed to determine the growth pattern of algae. Moreover, the methane potential of harvested biomass was calculated. The algal strains were incubated for 18 days, and the growth kinetics were determined. After the incubation, the biomass was harvested and assessed for its chemical oxygen demand content and biomethane potential. Among the tested strains, C. sorokiniana was the best in biomass productivity (111.97 ± 0.9 mg L^-1d^-1). The calculated vegetation indices, namely; colorimetric difference, color index vegetation, vegetative, excess green, excess green minus excess red, combination, and brown index values showed a significant correlation with biomass and chlorophyll content. Among the tested growth models, the modified Gompertz shows the best growth pattern. Further, the estimated theoretical CH₄ yield was highest for C. minutum (0.98 mL g^-1) compared to other tested strains. The present findings suggest that image analysis can be used as an alternative method to study the growth kinetics and biomass production potential of different algae during cultivation in wastewater.

Production of pyruvic acid with Candida glabrata using self-fermenting spent yeast cell dry powder as a seed nitrogen source

Pyruvic acid is an important organic acid and a key industrial raw material. It is widely used in the chemical, agricultural, and food fields. Candida glabrata is the preferred strain for pyruvic acid production. The waste yeast cell for pyruvic acid fermentation with C. glabrata are rich in protein, amino acid, nucleic acid, and vitamins, as potential and cost-effective nitrogen source raw material. In this study, the potential of C. glabrata to produce pyruvic acid using spent yeast cell dry powder was evaluated. When 30 g/L of spray-dried spent yeast cell powder was used as the seed nitrogen source, a high titer of pyruvic acid was obtained. The pyruvic acid production reached 63.4 g/L with a yield of 0.59 g/g in a 5 L bioreactor. After scale-up to a 50 L bioreactor using the fermented spent yeast cell dry powder as a seed nitrogen source, 65.1 g/L of pyruvic acid was harvested, with a yield of 0.61 g/g. This study proposes a promisingapproach for increasing the pyruvic acid titer and reducing the costs.

A low-cost system for monitoring pH, dissolved oxygen and algal density in continuous culture of microalgae

In a continuous and closed system of culturing microalgae, constantly monitoring and controlling pH, dissolved oxygen (DO) and microalgal density in the cultivation environment are paramount, which ultimately influence on the growth rate and quality of the microalgae products. Apart from the pH and DO parameters, the density of microalgae can be used to contemplate what light condition in the culture chamber is or when nutrients should be supplemented, which both also decide productivity of the cultivation. Moreover, the microalgal density is considered as an indicator indicating when the microalgae can be harvested. Therefore, this work proposes a low-cost monitoring equipment that can be employed to observe pH, DO and microalgal density over time in a culture environment. The measurements obtained by the proposed monitoring device can be utilized for not only real-time observations but also controlling other sub-systems in a continuous culture model including stirring, ventilating, nutrient supplying and harvesting, which leads to more efficiency in the microalgal production. More importantly, it is proposed to utilize the off-the-shelf materials to fabricate the equipment with a total cost of about 513 EUR, which makes it practical as well as widespread. The proposed monitoring apparatus was validated in a real-world closed system of cultivating a microalgae strain of Chlorella vulgaris. The obtained results indicate that the measurement accuracies are 0.3%, 3.8% and 8.6% for pH, DO and microalgae density quantities, respectively.

Strategy Development for Microalgae Spirulina platensis Biomass Cultivation in a Bubble Photobioreactor to Promote High Carbohydrate Content

As a counter to climate change, energy crises, and global warming, microalgal biomass has gained a lot of interest as a sustainable and environmentally favorable biofuel feedstock. Microalgal carbohydrate is considered one of the promising feedstocks for biofuel produced via the bioconversion route under a biorefinery system. However, the present culture technique, which uses a commercial medium, has poor biomass and carbohydrate productivity, creating a bottleneck for long-term microalgal-carbohydrate-based biofuel generation. This current investigation aims toward the simultaneous increase in biomass and carbohydrate accumulation of Spirulina platensis by formulating an optimal growth condition under different concentrations of nitrogen and phosphorous in flasks and a bubble photobioreactor. For this purpose, the lack of nitrogen (NaNO3) and phosphorous (K2HPO4) in the culture medium resulted in an enhanced Spirulina platensis biomass and total carbohydrate 0.93 ± 0.00 g/L and 74.44% (w/w), respectively. This research is a significant step in defining culture conditions that might be used to tune the carbohydrate content of Spirulina.

Global market and economic analysis of microalgae technology: Status and perspectives

Microalgae have been a promising alternative source of high-value compounds to replace the non-sustainable fossil fuels resource. The recent research development of algae-based bioproducts has remarkable impact various industries section for its renewability, efficiency, and environmentally friendly crops over those synthetic-made product. However, by utilizing microalgae biomass toward their full potential is still limited due to lack of research funding, social acceptability and challenges in policy implementation. This present review highlights the various microalgae biotechnology with consideration of economical aspect for the global potential of algae market, comparison between the microalgae market in Malaysia and international countries. In addition, the cultivation technologies and feasibility of microalgae biomass production globally, followed by insightful challenges and future development of microalgae industry are mentioned. The current study will contribute to the understanding of upstream and downstream of microalgae processing along with technical economical understandings for the successful commercialisation of microalgae products.