Biomass, noted for its adaptability, has various applications in biofuel generation, industrial use, and environmental cleaning. This study looks into the multiple roles of biomass as a renewable energy source, with a particular emphasis on its vital contribution to biofuel production. Through a thorough evaluation of different conversion routes—thermal, biological, and physical—the study emphasizes thermochemical processes' efficiency, cost-effectiveness, and adaptability. Notably, technologies like gasification and quick pyrolysis are thoroughly investigated, followed by in-depth discussions of reactor optimization strategies to enhance performance and output. The complex structure of biomass, which is dominated by high-molecular-weight polysaccharides such as cellulose and hemicelluloses, demonstrates its significant potential for energy generation. Furthermore, the study categorizes biomass by content, origin, and conversion processes, resulting in a comprehensive inventory of available resources. Biomass from the agriculture and forestry industries, such as starch, sugar, lignocellulose, and organic wastes, is rigorously analyzed for energy production. Furthermore, various biomass processing techniques, including thermochemical, biochemical, and physicochemical conversions, are carefully tested in real-world applications to ensure their efficacy and viability. Beyond its importance in biofuel production, the article underlines biomass' versatility in satisfying industrial needs and contributing to environmental cleanup initiatives. This study lays the groundwork for informed decision-making and innovative solutions in various industries by providing a thorough understanding of biomass's various benefits and applications, including energy provision, industrial processes, and ecological restoration. 

Biomass: A Versatile Resource for Biofuel, Industrial, and Environmental Solution

Over recent years, many companies and countries have established net-zero emission objectives for 2050 or sooner. Frankly, there will be fraught with challenges and dangers to some extent to attain net-zero. Therefore, we scrutinized the importance of net-zero strategies and plans/roadmap to attain these net-zero goals in this review. We found that overcoming the diverse obstacles including settling on a formal definition of the concept, increasing global financing and infrastructure investments, and ensuring that advancements in green technology occur while keeping their costs low or subsidizing them is very imperative to quickly transition away from carbon-emitting fossil fuels. Other challenges could include getting the net-zero ball moving on difficult-to-decarbonize sectors, choosing the correct carbon offsets, not relying solely on renewable energy credits, and striking the right balance between climate-related policies at various levels. Based on the review analysis, we suggested some solutions to achieving net-zero by 2050, as well as long-run scenarios. In short, all components of sustainable development, socioeconomic sustainability, or the pursuit of broad developing opportunities must be matched with a net-zero emission-based economy, this ensures stability and harmony in the balance between national targets and international benefits. 

Track to reach net-zero: Progress and pitfalls

Integrating machine learning (ML) and artificial intelligence (AI) with renewable energy sources, including biomass, biofuels, engines, and solar power, can revolutionize the energy industry. Biomass and biofuels have benefited significantly from implementing AI and ML algorithms that optimize feedstock, enhance resource management, and facilitate biofuel production. By applying insight derived from data analysis, stakeholders can improve the entire biofuel supply chain - including biomass conversion, fuel synthesis, agricultural growth, and harvesting - to mitigate environmental impacts and accelerate the transition to a low-carbon economy. Furthermore, implementing AI and ML in combustion systems and engines has yielded substantial improvements in fuel efficiency, emissions reduction, and overall performance. Enhancing engine design and control techniques with ML algorithms produces cleaner, more efficient engines with minimal environmental impact. This contributes to the sustainability of power generation and transportation. ML algorithms are employed in solar energy to analyze vast quantities of solar data to improve photovoltaic systems' design, operation, and maintenance. The ultimate goal is to increase energy output and system efficiency. Collaboration among academia, industry, and policymakers is imperative to expedite the transition to a sustainable energy future and harness the potential of AI and ML in renewable energy. By implementing these technologies, it is possible to establish a more sustainable energy ecosystem, which would benefit future generations.

Harnessing a Better Future: Exploring AI and ML Applications in Renewable Energy

Influence of enzymatic crosslinking on Tambaqui (Colossoma macropomum) skin gelatin film properties

Examining the game-changing possibilities of explainable machine learning techniques, this study explores the fast-growing area of biochar production prediction. The paper demonstrates how recent advances in sensitivity analysis methodology, optimization of training hyperparameters, and state-of-the-art ensemble techniques have greatly simplified and enhanced the forecasting of biochar output and composition from various biomass sources. The study argues that white-box models, which are more open and comprehensible, are crucial for biochar prediction in light of the increasing suspicion of black-box models. Accurate forecasts are guaranteed by these explainable AI systems, which also give detailed explanations of the mechanisms generating the outcomes. For prediction models to gain confidence and for biochar production processes to enable informed decision-making, there must be an emphasis on interpretability and openness. The paper comprehensively synthesizes the most critical features of biochar prediction by a rigorous assessment of current literature and relies on the authors' own experience. Explainable machine learning techniques encourage ecologically responsible decision-making by improving forecast accuracy and transparency. Biochar is positioned as a crucial participant in solving global concerns connected to soil health and climate change, and this ultimately contributes to the wider aims of environmental sustainability and renewable energy consumption. 

Improving the prediction of biochar production from various biomass sources through the implementation of eXplainable machine learning approaches

In 2020, it is projected that the number of households in the Kepulauan Mentawai  Regency will reach 22,534 units in which 9,471 units have received electricity from the State Electricity Company (PLN). By the end of 2020, as many as 1,244 households will be targeted to get electricity from the bamboo biomass power plant that has been built. Thus until the end of 2020, there were still 11,819 household units that would not yet get electricity. The Kepulauan Mentawai  Regency Government targets that by the end of 2035 all households will have electricity, by developing electricity using bamboo biomass raw materials. The number of households in the Kepulauan Mentawai  Regency until the end of 2035 is projected to be 28,639 units, of which from 2020 to the end of 2035, there will be 17,924 household units including electricity needs. If each household is targeted to get 450 VA power, then the total electricity power still needed is 8,066 KVA or approximately 70,758,160 kWh/year. The bamboo biomass power plant, which will be operated in the Kepulauan Mentawai Regency, currently requires 1.5 kg of dry bamboo to produce 1 kW power with a power factor ( ghospi ) of 0.8. Thus the need for bamboo for power plants is approximately 132,484.1 tons/year. Therefore, to provide this bamboo supply, approximately 714 ha of bamboo is needed.

/pdf/design-of-sustainable-agricultural-based-biomass-vthz749jwv.pdf

Design of Sustainable Agricultural-Based Biomass Electrification Model in the Islands Area: Prospect of Bamboo Biomass

Gelatin is a type of protein obtained from natural collagen found in skin and bones. This study aimed to see the effect of different types of acids on the characteristics of the gelatin produced from tuna fish bones. The method used in this research was exploratory with 3 treatments, which was used 3 types of acid solutions such as CH3COOH 3%, H3PO4 3% and HCL 3% in the gelatin extraction process. The obtained gelatin was yielded and analyzed to see the water content, ash content, protein content and pH value. The result showed that the obtained gelatin has different characteristics for each treatment. Extraction using 3% HCL produced the highest gelatin yield was 10.1%, while the extraction with CH3COOH 3% resulted gelatin in the highest protein content of 29.21%. In addition, extraction using H3PO4 3% produced gelatin with characteristics not much different from HCL 3%.

https://iopscience.iop.org/article/10.1088/1755-1315/1059/1/012049/pdf

Extraction of Gelatin from Tuna Fish Bones (Thunnus sp) on Variation of Acid Solution

MOCAF is the result of processing cassava flour by fermentation, so it has better physicochemical characteristics than cassava flour. Often used as a substitute for flour in food processing, but the protein content is low. The use of MOCAF to make cakes needs to be mixed with other flour, such as corn flour to increase the protein content of cakes. This study aims to determine the effect of a mixture of MOCAF and corn flour on the characteristics of the resulting chiffon cake. Observations were made on the physical, chemical, sensory, and microbiological properties. Based on the analysis of physical, chemical, and sensory tests (color, aroma, taste, and texture) chiffon cake with a ratio of 70% MOCAF and 30% corn flour was the best treatment with moisture content (32.96%), ash (1.31). %), protein (6.34%), fat (17.14%), carbohydrates (42.20%), fiber (2.64%), a ratio of chiffon cake (93.76%), swelling power (78, 82%), color (83.45oHue), hardness (4.25 N/Cm2), microbes (3.83 log CFU/g), and mold (2.46 log CFU/g).

https://iopscience.iop.org/article/10.1088/1755-1315/1182/1/012052/pdf

The Comparative Effect of MOCAF (Modified Cassava Flour) and Corn (Zea mays L.) Flour on the Characteristics of Chiffon Cake

This study aims to determine the chemical characteristics and antioxidant activity of instant fish ball flour from tuna trimmings with the addition of various extracts of natural ingredients. This study used an exploratory method with 4 treatments, namely the addition of purple sweet potato extract, root tuber extract, carrot extract, butterfly pea extract and control. The results obtained were instant fish ball flour has protein content of about 19%, water content of about 8%, fat content of about 3%, ash content of about 1.5% and carbohydrate content of about 67-68% for all treatments. In addition, the fish ball flour obtained also had antioxidant activity of 8.47, 13.87, 25.27, 22.96, 17.64 ppm / 100 ppm for the control treatment, purple sweet potato, beetroot, carrot and butterfly pea, respectively. Overall the fish ball flour obtained has sufficient nutritional value and also functional value that can be used in the manufacture of fish balls.

https://iopscience.iop.org/article/10.1088/1755-1315/1182/1/012064/pdf

Characteristics and Antioxidant Activities of Instant Fish Ball Flour from Tuna Trimmings with Addition of Various Extracts from Natural Ingredients

Physicochemical Characteristics Analysis of Biomass and Calorie Values of Several Types of Bamboo in Typologies of Altitude in Different Growing Sites

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