Microwave vacuum pyrolysis of palm kernel shell was examined to produce engineered biochar for application as additive in agriculture application. The pyrolysis approach, performed at 750 W of microwave power, produced higher yield of porous biochar (28 wt%) with high surface area (270 cm2/g) compared to the yield obtained by conventional approach (<23 wt%). Addition of the porous biochar in mushroom substrate showed increased moisture content (99%) compared to the substrate without biochar (96%). The mushroom substrate added with biochar (150 g) was optimal in shortening formation, growth, and full colonization of the mycelium within one month. Using 2.5% of the biochar in mushroom substrate desirably maintained the optimum pH level (6.8-7) during the mycelium colonization period, leading to high mycelium growth (up to 91%) and mushroom yield (up to 280 g). The engineered biochar shows great potential as moisture retention and neutralizing agent in mushroom cultivation.
Phycoremediation is gaining attention not only as a pollutant mitigation approach but also as one of the most cost-effective paths to achieve carbon neutrality. When compared to conventional treatment methods, phycoremediation is highly effective in removing noxious substances from wastewater and is inexpensive, eco-friendly, abundantly available, and has many other advantages. The process results in valuable bioproducts and bioenergy sources combined with pollutants capture, sequestration, and utilization. In this review, microalgae-based phycoremediation of various wastewaters for carbon neutrality and circular economy is analyzed scientometrically. Different mechanisms for pollutants removal and resource recovery from wastewaters are explained. Further, critical parameters that influence the engineering design and phycoremediation performance are described. A comprehensive knowledge map highlighting the microalgae potential to treat a variety of industrial effluents is also presented. Finally, challenges and future prospects for industrial implementation of phycoremediation towards carbon neutrality coupled with circular economy are discussed.
The efforts have been made to review phyllosilicate derived (clay-based) heterogeneous catalysts for biodiesel production via lignocellulose derived feedstocks. These catalysts have many practical and potential applications in green catalysis. Phyllosilicate derived heterogeneous catalysts (modified via any of these approaches like acid activated clays, ion exchanged clays and layered double hydroxides) exhibits excellent catalytic activity for producing cost effective and high yield biodiesel. The combination of different protocols (intercalated catalysts, ion exchanged catalysts, acidic activated clay catalysts, clay-supported catalysts, composites and hybrids, pillared interlayer clay catalysts, and hierarchically structured catalysts) was implemented so as to achieve the synergetic effects (acidic-basic) in resultant material (catalyst) for efficient conversion of lignocellulose derived feedstock (non-edible oils) to biodiesel. Utilisation of these Phyllosilicate derived catalysts will pave path for future researchers to investigate the cost-effective, accessible and improved approaches in synthesising novel catalysts that could be used for converting lignocellulosic biomass to eco-friendly biodiesel.
Microalgae have emerged as an effective dual strategy for bio-valorisation of food processing wastewater and food waste hydrolysate which favours microalgae cultivation into producing value-added by products mainly lipids, carbohydrates, and proteins to the advantages of bioplastic production. Moreover, various microalgae have successfully removed high amount of organic pollutants from food processing wastewater prior discharging into the environment. Innovation of microalgae cultivating in food processing wastewater greatly reduced the cost of wastewater treatment compared to conventional approach in terms of lower carbon emissions, energy consumption, and chemical usage while producing microalgae biomass which can benefit low-cost fertilizer and bioplastic applications. The study on several microalgae species has all successfully grown on food waste hydrolysates showing high exponential growth rate and biomass production rich in proteins, lipids, carbohydrates, and fatty acids. Multiple techniques have been implemented for the extraction of food wastes to be incorporate into the bioplastic production.
Microbial fermentation of organic matter under anaerobic conditions is currently the prominent pathway for biohydrogen production. Organic matter present in waste residues is regarded as an economic feedstock for biohydrogen production by dark and photo fermentative bacteria. Agricultural residues, fruit wastes, vegetable wastes, industrial wastewaters, and other livestock residues are some of the organic wastes most commonly used for biohydrogen production due to their higher organic content and biodegradability. Appropriate pretreatments are required to enhance the performance of biohydrogen from complex organic wastes. Biohydrogen production could also be enhanced by optimizing operation conditions and the addition of essential nutrients and nanoparticles. This review describes the pathways of biohydrogen production, discusses the effect of organic waste sources used and microbes involved on biohydrogen production, along with addressing the key parameters, advantages, and difficulties in each biohydrogen production pathway.
Biofuels have become an attractive energy source because of the growing energy demand and environmental issues faced by fossil fuel consumption. Algal biomass, particularly microalgae, has excellent potential as feedstock to be converted to bio-oil, biochar, and combustible syngas via thermochemical conversion processes. Third-generation biofuels from microalgal feedstock are the promising option, followed by the first-generation and second-generation biofuels. This paper provides a review of the applications of thermochemical conversion techniques for biofuel production from algal biomass, comprising pyrolysis, gasification, liquefaction, and combustion processes. The progress in the thermochemical conversion of algal biomass is summarized, emphasizing the application of pyrolysis for its benefits over other processes. The review also encompasses the challenges and perspectives associated with the valorization of microalgae to biofuels ascertaining the potential opportunities and possibilities of extending the research into this area.
The litter of persistent organic pollutants (POPs) into the water streams and soil bodies via industrial effluents led to several adverse effects on the environment, health, and ecosystem. For the past decades, scientists have been paying efforts in the innovation and development of POPs removal from wastewater treatment. However, the conventional methods used for the removal of POPs from wastewater are costly and could lead to secondary pollution including soil and water bodies pollution. In recent, the utilization of green mechanisms such as biosorption, bioaccumulation and biodegradation has drawn attention and prelude the potential of green technology globally. Microalgae-bacteria consortia have emerged to be one of the latent wastewater treatment systems. The synergistic interactions between microalgae and bacteria could proficiently enhance the existing biological wastewater treatment system. This paper will critically review the comparison of conventional and recent advanced wastewater treatment systems and the mechanisms of the microalgae-bacteria symbiosis system.
This research work aims to fabricate an optimized up-scaled photobioreactor and extraction tank which incorporates the Internet of Things (IoT) for remote monitoring of selected parameters without being present in the lab as the industry is gradually moving towards the direction of remote operation. Several design factors were considered where modelling using ANSYS was carried out before the finalised design is drawn using AutoCAD. To monitor critical parameters that include liquid level, temperature, and pH condition during the operation of the tanks, water-proof sensors are implemented with the aid of Arduino NodeMCU board and the sensors are linked with Blynk, a smartphone application that allows remote monitoring via Wi-Fi connection. The sensors' results obtained using the Blynk application show high accuracy as compared with manual data except for photobioreactor liquid level. This shows that IoT and remote monitoring can be integrated successfully.
Algal biomass is known as a promising sustainable feedstock for the production of biofuels and other valuable products. However, since last decade, massive amount of interests have turned to converting algal biomass into biochar. Due to their high nutrient content and ion-exchange capacity, algal biochars can be used as soil amendment for agriculture purposes or adsorbents in wastewater treatment for the removal of organic or inorganic pollutants. This review describes the conventional (e.g., slow and microwave-assisted pyrolysis) and newly developed (e.g., hydrothermal carbonization and torrefaction) methods used for the synthesis of algae-based biochars. The characterization of algal biochar and a comparison between algal biochar with biochar produced from other feedstocks are also presented. This review aims to provide updated information on the development of algal biochar in terms of the production methods and the characterization of its physical and chemical properties to justify and to expand their potential applications.
Liquid Biphasic Flotation (LBF) is an advanced recovery method that has been effectively applied for biomolecules extraction. The objective of this investigation is to incorporate the fermentation and extraction process of lipase from Burkholderia cepacia using flotation system. Initial study was conducted to compare the performance of bacteria growth and lipase production using flotation and shaker system. From the results obtained, bacteria shows quicker growth and high lipase yield via flotation system. Integration process for lipase separation was investigated and the result showed high efficiency reaching 92.29% and yield of 95.73%. Upscaling of the flotation system exhibited consistent result with the lab-scale which are 89.53% efficiency and 93.82% yield. The combination of upstream and downstream processes in a single system enables the acceleration of product formation, improves the product yield and facilitates downstream processing. This integration system demonstrated its potential for biomolecules fermentation and separation that possibly open new opportunities for industrial production.
Artificial intelligence (AI) and machine learning (ML) are currently used in several areas. The applications of AI and ML based models are also reported for monitoring and design of biological wastewater treatment systems (WWTS). The available information is reviewed and presented in terms of bibliometric analysis, model's description, specific applications, and major findings for investigated WWTS. Among the applied models, artificial neural network (ANN), fuzzy logic (FL) algorithms, random forest (RF), and long short-term memory (LSTM) were predominantly used in the biological wastewater treatment. These models are tested by predictive control of effluent parameters such as biological oxygen demand (BOD), chemical oxygen demand (COD), nutrient parameters, solids, and metallic substances. Following model performance indicators were mainly used for the accuracy analysis in most of the studies: root mean squared error (RMSE), mean square error (MSE), and determination coefficient (DC). Besides, outcomes of various models are also summarized in this study.
The identification of microalgae species is an important tool in scientific research and commercial application to prevent harmful algae blooms (HABs) and recognizing potential microalgae strains for the bioaccumulation of valuable bioactive ingredients. The aim of this study is to incorporate rapid, high-accuracy, reliable, low-cost, simple, and state-of-the-art identification methods. Thus, increasing the possibility for the development of potential recognition applications, that could identify toxic-producing and valuable microalgae strains. Recently, deep learning (DL) has brought the study of microalgae species identification to a much higher depth of efficiency and accuracy. In doing so, this review paper emphasizes the significance of microalgae identification, and various forms of machine learning algorithms for image classification, followed by image pre-processing techniques, feature extraction, and selection for further classification accuracy. Future prospects over the challenges and improvements of potential DL classification model development, application in microalgae recognition, and image capturing technologies are discussed accordingly.
The increase in worldwide demand for energy is driven by the rapid increase in population and exponential economic development. This resulted in the fast depletion of fossil fuel supplies and unprecedented levels of greenhouse gas in the atmosphere. To valorize biomass into different bioproducts, one of the popular and carbon-neutral alternatives is biorefineries. This system is an appropriate technology in the circular economy model. Various research highlighted the role of biorefineries as a centerpiece in the carbon-neutral ecosystem of technologies of the circular economy model. To fully realize this, various improvements and challenges need to be addressed. This paper presents a critical and timely review of the challenges and future direction of biorefineries as an alternative carbon-neutral energy source.
The valorization of organosolv pretreatment (OP) is a required approach to the industrialization of the current enzyme-mediated lignocellulosic biorefinery. Recent literature has demonstrated that the solvolysis happening in the OP can modify the soluble components into value-added active compounds, namely organosolv modified lignin (OML) and organosolv modified sugars (OMSs), in addition to protecting them against excessive degradation. Among them, the OML is coincidental with the "lignin-first" strategy that should render a highly reactive lignin enriched with β-O-4 linkages and less condensed structure by organosolv grafting, which is desirable for the transformation into phenolic compounds. The OMSs are valuable glycosidic compounds mainly synthesized by trans-glycosylation, which can find potential applications in cosmetics, foods, and healthcare. Therefore, a state-of-the-art OP holds a big promise of lowering the process cost by the valorization of these active compounds. Recent advances in organosolv modified components are reviewed, and perspectives are made for addressing future challenges.
Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.
Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.
Microalgae are photoautotrophic microorganisms which comprise of species from several phyla. Microalgae are promising in producing a varieties of products, including food, feed supplements, chemicals, and biofuels. Medicinal supplements derived from microalgae are of a significant market in which compounds such as -carotene, astaxanthin, polyunsaturated fatty acids (PUFA) such as docosahexaenoic acid (DHA) and eicosapentaenoic acid (EPA), and polysaccharides such as -glucan, are prominent. Microalgae species which are commonly applied for commercial productions include Isochrysis sp., Chaetoceros (Chlorella sp.), Arthrospira sp. (Spirulina Bioactive) and many more. In this present review, microalgae species which are feasible in metabolites production are being summarized. Metabolites produced by microalgae as well as their prospective applications in the healthcare and pharmaceutical industries, are comprehensively discussed. This evaluation is greatly assisting industrial stakeholders, investors, and researchers in making business decisions, investing in ventures, and moving the production of microalgae-based metabolites forward.
Steam gasification of microalgae upon perovskite oxide-supported nickel (Ni) catalysts was carried out for H2-rich gas production. Ni-perovskite oxide catalysts with partial substitution of B in perovskite structures (Ni/CaZrO3, Ni/Ca(Zr0.8Ti0.2)O3, and Ni/Ca(Zr0.6Ti0.4)O3) were synthesized and compared with those of the Ni/Al2O3 catalyst. The perovskite oxide supports improved Ni dispersion by reducing the particle size and strengthening the Ni-support interaction. Higher gas yields and H2 selectivity were obtained using Ni-perovskite oxide catalysts rather than Ni/Al2O3. In particular, Ni/Ca(Zr0.8Ti0.2)O3 showed the highest activity and selectivity for H2 production because of the synergetic effect of metallic Ni and elements present in the perovskite structures caused by high catalytic activity coupled with enhanced oxygen mobility. Moreover, increasing the temperature promoted the yield of gas and H2 content. Overall, considering the outstanding advantages of perovskite oxides as supports for Ni catalysts is a promising prospect for H2 production via gasification technology.
The rapid expansion of industrialization and continuous population growth have caused a steady increase in energy consumption. Despite using renewable energy, such as bioethanol, to replace fossil fuels had been strongly promoted, however the outcomes were underwhelming, resulting in excessive greenhouse gases (GHG) emissions. Microalgal biochar, as a carbon-rich material produced from the pyrolysis of biomass, provides a promising solution for achieving net zero emission. By utilizing microalgal biochar, these GHG emissions can be captured and stored efficiently. It also enhances soil fertility, improves water retention, and conduct bioremediation in agriculture and environmental remediation field. Moreover, incorporating microalgal biochar into a zero-waste biorefinery could boost the employ of biomass feedstocks effectively to produce valuable bioproducts while minimizing waste. This contributes to sustainability and aligns with the concepts of a circular bioeconomy. In addition, some challenges like commercialization and standardization will be addressed in the future.