The valorization and conversion of biomass into various value-added products and bioenergy play an important role in the realization of sustainable circular bioeconomy and net zero carbon emission goals. To that end, microwave technology has been perceived as a promising solution to process and manage oil palm waste due to its unique and efficient heating mechanism. This review presents an in-depth analysis focusing on microwave-assisted torrefaction, gasification, pyrolysis and advanced pyrolysis of various oil palm wastes. In particular, the products from these thermochemical conversion processes are energy-dense biochar (that could be used as solid fuel, adsorbents for contaminants removal and bio-fertilizer), phenolic-rich bio-oil, and H2-rich syngas. However, several challenges, including (1) the lack of detailed study on life cycle assessment and techno-economic analysis, (2) limited insights on the specific foreknowledge of microwave interaction with the oil palm wastes for continuous operation, and (3) effects of tunable parameters and catalyst's behavior/influence on the products' selectivity and overall process's efficiency, remain to be addressed in the context of large-scale biomass valorization via microwave technology.
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.
A new cutting-edge lignocellulose fractionation technology for the co-production of glucose, native-like lignin, and furfural was introduced using mannitol (MT)-assisted p-toluenesulfonic acid/pentanol pretreatment, as an eco-friendly process. The addition of optimized 5% MT in pretreatment enhanced the delignification rate by 29% and enlarged the surface area and biomass porosity by 1.07-1.80 folds. This increased the glucose yield by 45% (from 65.34 to 94.54%) after enzymatic hydrolysis relative to those without MT. The extracted lignin in the organic phase of pretreatment exhibited β-O-4 bonds (61.54/100 Ar) properties of native cellulosic enzyme lignin. Lignin characterization and molecular docking analyses revealed that the hydroxyl tails of MT were incorporated with lignin and formed etherified lignin, which preserved high lignin integrity. The solubilized hemicellulose (96%) in the liquid phase of pretreatment was converted into furfural with a yield of 83.99%. The MT-assisted pretreatment could contribute to a waste-free biorefinery pathway toward a circular bioeconomy.
In this study, the cultivation and harvesting of Arthrospira platensis biomass were proposed via simple, safe, and efficient techniques for direct consumption. Cultivation of microalgae in a covered macrobubble column under outdoor conditions resulted in significant differences (p
Air gasification of the Wood-Plastic Composite (WPC) was performed over Ni-loaded HZSM-5 catalysts to generate H2-rich gas. Increasing SiO2/Al2O3 ratio (SAR) of HZSM-5 adversely affected catalytic activity, where the highest gas yield (51.38 wt%) and H2 selectivity (27.01 vol%) were acquired using 20 %Ni/HZSM-5(30) than those produced over 20 %Ni/HZSM-5(80) and 20 %Ni/HZSM-5(280). Reducing SAR was also favorably conducive to increasing the acyclic at the expense of cyclic compounds in oil products. These phenomena are attributed to enhanced acid strength and Ni dispersion of 20 %Ni/HZSM-5(30) catalyst. Moreover, catalytic activity in the terms of gas yield and H2 selectivity enhanced with growing Ni loading to 20 %. Also, the addition of promoters (Cu and Ca) to 20 %Ni/HZSM-5(30) boosted the catalytic efficiency for H2-rich gas generation. Raising temperature indicated a positive relevance with the gas yield and H2 selectivity. WPC valorization via gasification technology would be an outstanding outlook in the terms of a waste-to-energy platform.
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.
Substrate toxicity would limit the upgrading of waste biomass to medium-chain fatty acids (MCFAs). In this work, two fermentation modes of electro-fermentation (EF) and traditional fermentation (TF) with different concentration of liquor fermentation waste (20%, 40%, 60%) were used for MCFAs production as well as mechanism investigation. The highest caproate (4.04 g/L) and butyrate (13.96 g/L) concentrations were obtained by EF at 40% substrate concentration. TF experiments showed that the substrate concentration above 40% severely inhibited ethanol oxidation and products formation. Compared with TF mode, the total substrates consumption and product yields under EF mode were significantly increased by 2.6%-43.5% and 54.0%-83.0%, respectively. Microbial analysis indicated that EF effectively alleviated substrate toxicity and enriched chain elongation bacteria, particularly Clostridium_sensu_stricto 12, thereby promoting ethanol oxidation and products formation. Caproiciproducens tolerated high-concentration substrates to ensure normal lactate metabolism. This study provides a new way to produce MCFAs from high concentration wastewater.
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.
Many energy-intensive processes are employed to enhance biomass fuel properties to overcome the difficulties in utilizing biomass as fuel. Therefore, energy conservation during these processes is crucial for realizing a circular bioeconomy. This study develops a newly devised method to evaluate SCG biochars' higher heating value (HHV) and predict moisture content from power consumption. It is found that the increasing rates of HHV immediately follow decreases in power consumption, which could be used to determine the pretreatment time for energy conservation. The non-dominated sorting genetic algorithm II (NSGA-II) maximizes SCG biochar's HHV while minimizing energy consumption. The results show that producing SCG biochar with 23.98 MJ∙kg-1 HHV requires 20.042 MJ∙kg-1, using a torrefaction temperature of 244 °C and torrefaction time of 27 min and 43 sec. Every kilogram of biochar with an energy yield of 85.93 % is estimated to cost NT$ 12.21.
This study presented a novel methodology to predict microalgae chlorophyll content from colour models using linear regression and artificial neural network. The analysis was performed using SPSS software. Type of extractant solvents and image indexes were used as the input data for the artificial neural network calculation. The findings revealed that the regression model was highly significant, with high R2 of 0.58 and RSME of 3.16, making it a useful tool for predicting the chlorophyll concentration. Simultaneously, artificial neural network model with R2 of 0.66 and low RMSE of 2.36 proved to be more accurate than regression model. The model which fitted to the experimental data indicated that acetone was a suitable extraction solvent. In comparison to the cyan-magenta-yellow-black model in image analysis, the red-greenblue model offered a better correlation. In short, the estimation of chlorophyll concentration using prediction models are rapid, more efficient, and less expensive.
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.
A novel process for simultaneous production of furfural and pretreatment of oil palm empty fruit bunch (EFB) by dilute acid pre-hydrolysis was developed based on non-isothermal kinetic modeling. Mass transfer analysis suggested that the internal diffusion could be neglected as diffusion time of sulfuric acid in EFB particles was significantly shorter than the pre-hydrolysis period, whereas the heating stage could not be neglected due to a significant part of xylan was solubilized at the stage. A strategy for increasing furfural yield was developed by intermittent discharging of steam, resulting in 71.4 % furfural yield. The pretreated solids showed good enzymatic digestibility. 136.3 g/L glucose corresponding to 81.6 % yield was obtained by high-solid loading hydrolysis. 95.4 g furfural and 212 g glucose could be obtained from 1 kg dry EFB. Therefore, non-isothermal effects on polysaccharide hydrolysis and pentose decomposition should be considered carefully for an efficient process design of EFB biorefining.
Versatility and desirable attributes of synthetic plastics have greatly contributed towards their wide applications. However, vast accumulation of plastic wastes in environment as a result of their highly recalcitrant nature has given rise to plastic pollution. Existing strategies in alleviating plastic wastes accumulation are inadequate, and there is a pressing need for alternative sustainable approaches in tackling plastic pollution. In this context, plastic biodegradation has emerged as a sustainable and environmental-friendly approach in handling plastic wastes accumulation, due to its milder and less energy-intensive conditions. In recent years, extensive research effort has focused on the identification of microorganisms and enzymes with plastic-degrading abilities. This review aims to provide a timely and holistic view on the current status of plastic biodegradation, focusing on recent breakthroughs and discoveries in this field. Furthermore, current challenges associated to plastic biodegradation are discussed, and the future perspectives for continuous advancement of plastic biodegradation are highlighted.
The ideal conditions for anaerobic digestion experiments with biochar addition are challenging to thoroughly study due to different experimental purposes. Therefore, three tree-based machine learning models were developed to depict the intricate connection between biochar properties and anaerobic digestion. For the methane yield and maximum methane production rate, the gradient boosting decision tree produced R2 values of 0.84 and 0.69, respectively. According to feature analysis, digestion time and particle size had a substantial impact on the methane yield and production rate, respectively. When particle sizes were in the range of 0.3-0.5 mm and the specific surface area was approximately 290 m2/g, corresponding to a range of O content (>31%) and biochar addition (>20 g/L), the maximum promotion of methane yield and maximum methane production rate were attained. Therefore, this study presents new insights into the effects of biochar on anaerobic digestion through tree-based machine learning.
The discovery of anammox process has provided eco-friendly and low-cost means of treating ammonia rich wastewater with remarkable efficiency. Furthermore, recent studies have shown that the possibility of operating the anammox process under low temperatures and high organic matter contents broadening the application of the anammox process. However, short doubling time and extensive levels of sensitivity towards nutrients and environmental alterations such as salinity and temperature are the limitations in practical applications of the anammox process. This review article provides the recent yet comprehensive viewpoint on anammox bacteria and the key perspectives in applying them as an efficient strategy for wastewater treatment.