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Insight into the Biomass-Based Briquette Generation from Agro-Residues: Challenges, Perspectives, and Innovations
- Published: 23 January 2024
- Volume 17 , pages 816–856, ( 2024 )
Cite this article
- Arshad Ali 1 ,
- Meena Kumari 2 ,
- Manisha 2 ,
- Sumit Tiwari 3 ,
- Mahesh Kumar 4 ,
- Deepak Chhabra 1 &
- Ravinder Kumar Sahdev ORCID: orcid.org/0000-0003-1705-9255 1
354 Accesses
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Turning biomass waste into briquettes using densification techniques is one of the most promising steps toward mitigating biomass waste pollution and fuel issues in developing countries. Despite the continuous growth of scientific output over the past few decades, only a limited amount of information is available in the literature on biomass briquette optimization and mathematical modeling, as well as the physiochemical characterization of biomass feedstocks and briquette operating variables. In light of this gap in the current literature, this study summarizes the current state of the art and recent advances in biomass-based briquettes generated from agro-residues as an alternative source of clean energy. The primary research method for this study is literature review and conceptual modeling. First, many densification processes, such as piston press, screw press, roller press, hydraulic press, and quality variables such as ash content, calorific value, moisture content, density, compressive strength, shatter index, etc., are thoroughly discussed and compared. Then characteristics of different biomass wastes are studied, together with process parameters, including temperature, type of binder used, particle size, and influence on densification process choice. The current evaluation concentrated on the mathematical modeling and optimization of the briquetting technology and the usefulness of briquettes in applications for heating, cooking, and energy production. Overall, this manuscript will help new researchers understand the basic methodology, classification, limitations, and future perspective of briquetting technology in the production of solid biofuels.
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Acknowledgements
The authors are thankful to the Department of Mechanical Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, Haryana, India, for providing the laboratory and internet facilities.
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Arshad Ali, Deepak Chhabra & Ravinder Kumar Sahdev
Department of Electrical Engineering, University Institute of Engineering and Technology, Maharshi Dayanand University, Rohtak, India, 124001
Meena Kumari & Manisha
Department of Mechanical Engineering, Shiv Nadar Institution of Eminence Deemed To Be University, Gautam Buddha Nagar, UP, India
Sumit Tiwari
Department of Mechanical Engineering, Guru Jambheshwar University of Science and Technology, Hisar, India
Mahesh Kumar
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Arshad Ali: Methodology, Writing – original draft. Ravinder Kumar Sahdev: Supervision, Conceptualization, Writing – review & editing. Deepak Chhabra: Supervision, Visualization, Writing – review & editing. Meena Kumari: Review and Editing. Sumit Tiwari : Review and Editing. Mahesh Kumar: Review and Editing. Manisha: Review and Editing.
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Ali, A., Kumari, M., Manisha et al. Insight into the Biomass-Based Briquette Generation from Agro-Residues: Challenges, Perspectives, and Innovations. Bioenerg. Res. 17 , 816–856 (2024). https://doi.org/10.1007/s12155-023-10712-5
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To Find Relative Earnings Gains After the China Shock, Look Outside Manufacturing and Upstream
We examine US workers' employment and earnings before and after trade liberalization with China. Among workers initially employed in manufacturing, we find substantial and persistent declines in both outcomes, with indirect exposure via input-output linkages exacerbating the negative effects of direct exposure. For workers initially employed outside manufacturing, however, we find that the positive impact of greater upstream exposure via inputs more than offsets the adverse impacts of own- and downstream exposure, inducing relative earnings gains. We also find that spatial exposure is more influential than industry exposure.
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Exploring the role of risk gene CNTN4 and APP in neuronal development
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We are excited to announce our recent publication, " CNTN4 Modulates Neural Elongation through Interplay with APP ," featured in Open Biology . This study delves into the intricate relationship between risk gene the neuronal cell molecule contactin-4 (CNTN4) and amyloid precursor protein (APP), elucidating their roles in neurodevelopmental disorders and Alzheimer's disease. We've detailed how CNTN4, a neuronal cell adhesion molecule, is instrumental in shaping neuronal morphology and spine density. Additionally, our findings reveal a co-dependent interaction between CNTN4 and APP crucial for neurite outgrowth, alongside a novel compensatory expression mechanism between these proteins.
What is the significance of CNTN4 and why did you choose to focus on this?
CNTN4 caught our attention during our research into 3p26 deletion syndrome—a condition linked with Autism Spectrum Disorders (ASD), as cited in Gandawijaya et al., 2021 . While CNTN4 is identified as a candidate gene for ASD, its functional roles were not well understood. This gap in knowledge spurred us to explore how CNTN4 functions within the brain, particularly its interactions with proteins involved in neurodegenerative diseases like Alzheimer's.
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Our research uncovered that CNTN4 not only contributes to neural elongation in the Frontal Cortex but also regulates its expression alongside APP, a protein implicated in Alzheimer's disease. It was quite remarkable to discover that CNTN4, a gene linked to developmental processes, also plays a role in modulating factors involved in Alzheimer's disease. This intersection of developmental and neurodegenerative pathways offers exciting new insights into the broader implications of these proteins.
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Looking ahead, our group is keen to further dissect the molecular mechanisms underpinning the interaction between CNTN4 and APP and explore their wider implications for disorders like Alzheimer's and ASD. Our next steps involve clarifying how the CNTN4-APP interaction impacts neural activity. Understanding this interaction is crucial as it represents a fundamental step towards a comprehensive grasp of neurodevelopmental and neurodegenerative disorders.
Group members at the University of Exeter including co-authors Josan Gandawijaya, Rosie Bamford and Madeline Eve.
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Our experience with Review Commons was exceptionally constructive. The streamlined peer review process facilitated a more efficient publication route, allowing us to refine our research with valuable feedback effectively. The impartial comments from reviewers who were not targeting a specific journal and the positive feedback aimed at enhancing our paper's quality were particularly striking. This process not only expedited our ability to share significant findings but also elevated the quality of our publication through rigorous peer evaluations. We are thoroughly satisfied with the outcome in Open Biology and are deeply appreciative of the genuine engagement from the journal in handling our revisions.
Dr. Oguro-Ando is a researcher and lecturer at the University of Exeter Medical School and has been interested in life science since she was a child, especially, how lives play rolls in plasticity to the environment. Asami’s group research aim is to further our understanding of the molecules, cells and circuits that underlie neurodevelopmental disorders affecting mental health including Autism is critical for developing more effective therapies for these disorders.
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Using ideas from game theory to improve the reliability of language models
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Imagine you and a friend are playing a game where your goal is to communicate secret messages to each other using only cryptic sentences. Your friend's job is to guess the secret message behind your sentences. Sometimes, you give clues directly, and other times, your friend has to guess the message by asking yes-or-no questions about the clues you've given. The challenge is that both of you want to make sure you're understanding each other correctly and agreeing on the secret message.
MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) researchers have created a similar "game" to help improve how AI understands and generates text. It is known as a “consensus game” and it involves two parts of an AI system — one part tries to generate sentences (like giving clues), and the other part tries to understand and evaluate those sentences (like guessing the secret message).
The researchers discovered that by treating this interaction as a game, where both parts of the AI work together under specific rules to agree on the right message, they could significantly improve the AI's ability to give correct and coherent answers to questions. They tested this new game-like approach on a variety of tasks, such as reading comprehension, solving math problems, and carrying on conversations, and found that it helped the AI perform better across the board.
Traditionally, large language models answer one of two ways: generating answers directly from the model (generative querying) or using the model to score a set of predefined answers (discriminative querying), which can lead to differing and sometimes incompatible results. With the generative approach, "Who is the president of the United States?" might yield a straightforward answer like "Joe Biden." However, a discriminative query could incorrectly dispute this fact when evaluating the same answer, such as "Barack Obama."
So, how do we reconcile mutually incompatible scoring procedures to achieve coherent, efficient predictions?
"Imagine a new way to help language models understand and generate text, like a game. We've developed a training-free, game-theoretic method that treats the whole process as a complex game of clues and signals, where a generator tries to send the right message to a discriminator using natural language. Instead of chess pieces, they're using words and sentences," says Athul Jacob, an MIT PhD student in electrical engineering and computer science and CSAIL affiliate. "Our way to navigate this game is finding the 'approximate equilibria,' leading to a new decoding algorithm called 'equilibrium ranking.' It's a pretty exciting demonstration of how bringing game-theoretic strategies into the mix can tackle some big challenges in making language models more reliable and consistent."
When tested across many tasks, like reading comprehension, commonsense reasoning, math problem-solving, and dialogue, the team's algorithm consistently improved how well these models performed. Using the ER algorithm with the LLaMA-7B model even outshone the results from much larger models. "Given that they are already competitive, that people have been working on it for a while, but the level of improvements we saw being able to outperform a model that's 10 times the size was a pleasant surprise," says Jacob.
"Diplomacy," a strategic board game set in pre-World War I Europe, where players negotiate alliances, betray friends, and conquer territories without the use of dice — relying purely on skill, strategy, and interpersonal manipulation — recently had a second coming. In November 2022, computer scientists, including Jacob, developed “Cicero,” an AI agent that achieves human-level capabilities in the mixed-motive seven-player game, which requires the same aforementioned skills, but with natural language. The math behind this partially inspired the Consensus Game.
While the history of AI agents long predates when OpenAI's software entered the chat in November 2022, it's well documented that they can still cosplay as your well-meaning, yet pathological friend.
The consensus game system reaches equilibrium as an agreement, ensuring accuracy and fidelity to the model's original insights. To achieve this, the method iteratively adjusts the interactions between the generative and discriminative components until they reach a consensus on an answer that accurately reflects reality and aligns with their initial beliefs. This approach effectively bridges the gap between the two querying methods.
In practice, implementing the consensus game approach to language model querying, especially for question-answering tasks, does involve significant computational challenges. For example, when using datasets like MMLU, which have thousands of questions and multiple-choice answers, the model must apply the mechanism to each query. Then, it must reach a consensus between the generative and discriminative components for every question and its possible answers.
The system did struggle with a grade school right of passage: math word problems. It couldn't generate wrong answers, which is a critical component of understanding the process of coming up with the right one.
“The last few years have seen really impressive progress in both strategic decision-making and language generation from AI systems, but we’re just starting to figure out how to put the two together. Equilibrium ranking is a first step in this direction, but I think there’s a lot we’ll be able to do to scale this up to more complex problems,” says Jacob.
An avenue of future work involves enhancing the base model by integrating the outputs of the current method. This is particularly promising since it can yield more factual and consistent answers across various tasks, including factuality and open-ended generation. The potential for such a method to significantly improve the base model's performance is high, which could result in more reliable and factual outputs from ChatGPT and similar language models that people use daily.
"Even though modern language models, such as ChatGPT and Gemini, have led to solving various tasks through chat interfaces, the statistical decoding process that generates a response from such models has remained unchanged for decades," says Google Research Scientist Ahmad Beirami, who was not involved in the work. "The proposal by the MIT researchers is an innovative game-theoretic framework for decoding from language models through solving the equilibrium of a consensus game. The significant performance gains reported in the research paper are promising, opening the door to a potential paradigm shift in language model decoding that may fuel a flurry of new applications."
Jacob wrote the paper with MIT-IBM Watson Lab researcher Yikang Shen and MIT Department of Electrical Engineering and Computer Science assistant professors Gabriele Farina and Jacob Andreas, who is also a CSAIL member. They presented their work at the International Conference on Learning Representations (ICLR) earlier this month, where it was highlighted as a "spotlight paper." The research also received a “best paper award” at the NeurIPS R0-FoMo Workshop in December 2023.
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MIT researchers have developed a new procedure that uses game theory to improve the accuracy and consistency of large language models (LLMs), reports Steve Nadis for Quanta Magazine . “The new work, which uses games to improve AI, stands in contrast to past approaches, which measured an AI program’s success via its mastery of games,” explains Nadis.
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This paper discusses biomass as a renewable energy source. ... BERA Biomass Energy Research Association, (www ... is a frequently discussed topic in the energy sector world due to the rising ...
Biomass & Bioenergy is an international journal publishing original research papers and short communications, review articles and case studies on biological resources, chemical and biological processes, and biomass products for new renewable sources of energy and materials. The scope of the …. View full aims & scope.
1. Introduction. In recent years, biomass materials have garnered increasing amounts of attention due to their excellent biocompatibility, high biodegradability, easy modification, low cost, and high sustainability [ 1, 2 ]. With the development of nanotechnology and biotechnology, these naturally abundant and easily regenerated materials ...
Biomass is an international, peer-reviewed, open access journal on biomass conversion and biorefinery published quarterly online by MDPI.. Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.; High Visibility: indexed within Scopus, EBSCO, and other databases. Rapid Publication: manuscripts are peer-reviewed and a first decision is ...
The activation energy values ranged from 81.919 to 262.238 and 85.737-212.349 kJ mol−1 and were generated by the KAS and FWO models and aided in understanding the biomass conversion process ...
The world's population continues to grow at a high rate, such that today's population is twice that of 1960, and is projected to increase further to 9 billion by 2050. This situation has brought about a situation in which the percentage of the global energy used in cities is increasing considerably. Biomass is a resource that is present in a variety of different materials: wood, sawdust ...
Biomass to bioenergy conversion is a well-reviewed topic. Numerous reviews and journal articles have been published to elucidate the technologies and their limitations; however, most of these papers are specic to a type of conversion technologies. Also, very few review articles include more novel technologies like dark and photo-fermentation, bio-
Bioenergy is a widespread form of modern renewable energy source because of the devastating impacts of high demand for fossil fuel, i.e., global warming and environmental effects. This paper addresses the different engineering aspects of bioenergy and its international status.
Here,we perform a bibliometric analysis of 9514 literature reports in the Web of Science Core Collection searched with the key words "Biomass energy" and "Environment*" date from 1998 to 2017; hot topics in the research and development of biomass energy utilization, as well as the status and development trends of biomass energy ...
This review paper focuses on biomass gasification ... Future research, including process optimization, reactor selection, low-cost catalyst preparation, and better selection of gasifying medium and technology, can exponentially increase the growth of the biomass era. Ni-based catalysts are extensively researched in the past years because of ...
Bugging out for jet fuel. As an alternative to fossil fuels for shipping, aviation and rocketry, high-energy polycyclopropanated fuels can be generated using a bacterial host. Stacey-Lynn Paiva ...
Photocatalytic reforming of biomass has emerged as an area of significant interest within the last decade. The number of papers published in the literature has been steadily increasing with keywords such as 'hydrogen' and 'visible' becoming prominent research topics. There are likely two primary drivers behind this, the first of which is that biomass represents a more sustainable ...
2. Hot Topics and Challenges of Biomass Catalysis. The transition from a fossil-based economy to a bio-based society represents a fun-damental goal to contrast key global issues, such as climate change and environmental pollution, and to reduce the strong dependency on fossil resources.
Turning biomass waste into briquettes using densification techniques is one of the most promising steps toward mitigating biomass waste pollution and fuel issues in developing countries. Despite the continuous growth of scientific output over the past few decades, only a limited amount of information is available in the literature on biomass briquette optimization and mathematical modeling, as ...
There are 3185 highly cited papers, accounting for 1.85% of the TPs (172,029). The TPs of USA in the field of biomass energy are 38,781 (see Table 1), of which there are 1204 papers included in ESI database. The ESI papers' output rate of USA is 3.10% which is more than the world average of 1.85%.
Research papers dealing with biomass energy potential upgrades and biomass-driven combined heat and power, co-firing, and combustion plants are most welcome. ... Research on this topic experienced significant development after 2000; moreover, the global trend of publications marked a significant increase after 2012. China and India have shown ...
This paper is the first in a three-part series examining the use of biomass as a fuel source, with emphasis on its potential use as a supplementary fuel for power gener-ation, using landfill gas fuelled, spark ignition gas en-gines. 1 Present address. MSE Ltd, Arle Crt, Hatherley Lane, Cheltenham.
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Publication Year: 1996: Title: On the relationship between plant species diversity and biomass: A comment on a paper by Gough, Grace and Taylor: DOI: 10.2307/3546258
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We examine US workers' employment and earnings before and after trade liberalization with China. Among workers initially employed in manufacturing, we find substantial and persistent declines in both outcomes, with indirect exposure via input-output linkages exacerbating the negative effects of direct exposure.
Biomass, as a part of renewables, is a resource found in large quantities and is a basis for many different industries. This paper presents the most important trends and characteristics of research in biomass potential and biomass utilization on a world scale. The main objective of this work is to analyze the state of research and trends in biomass potential and biomass utilization from 1974 ...
Nour Younis, a Brigham and Women's postdoc, and Nuria Puigmal, a Brigham and Women's postdoc and former MIT research affiliate, are the lead authors of the paper. The researchers are now working on launching a company to further develop the technology, led by Puigmal, who was recently awarded a Harvard Business School Blavatnik Fellowship.
Open Biology accepts papers via Review Commons saving authors time by facilitates quicker, informed decisions without restarting the peer review process. We are looking to publish more high-quality research articles in cellular and molecular biology. Find out more about our author benefits and submission process. Image credits:
Journal of Medical Internet Research Summary ALS Researchers face challenges in patient recruitment such as obtaining sufficient statistical powers and finding recruits that meet eligibility requirements; similarly, persons with ALS (PALS) face difficulty finding and enrolling in research studies for which they are eligible.
MIT researchers' "consensus game" is a game-theoretic approach for language model decoding. The equilibrium-ranking algorithm harmonizes generative and discriminative querying to enhance prediction accuracy across various tasks, outperforming larger models and demonstrating the potential of game theory in improving language model consistency and truthfulness.