A Short Overview of Analytical Techniques in Biomass Feedstock Characterization

Okafor, D. C.; Daramola, Michael O.

doi:10.1007/978-3-030-38032-8_2

Cited by 4 publications

(6 citation statements)

References 96 publications

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“…In the pyrolysis process, LCBs are exposed to temperatures ≥400 °C at low oxygen and possibly under a pressure‐controlled environment to convert them to gaseous, solid, and liquid fuels. In gasification, LCBs are subjected to temperatures greater than 700 °C in a controlled environment with partial steam or oxygen or both to convert them to gases (carbon dioxide, carbon monoxide, and hydrogen) 51 . The processing parameters of these technologies change with specific feedstocks.…”

Section: Application In Thermal Process Modelingmentioning

confidence: 99%

Artificial neural networks in valorization process modeling of lignocellulosic biomass

Pradhan

Jaiswal

2022

Biofuels Bioprod Bioref

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Value-added products such as biofuels, chemicals, enzymes, and many others can be prepared from lignocellulosic biomass (LCB). To achieve high yields of these value-added products, powerful tools such as artificial neural networks (ANN) and adaptive neuro-fuzzy inference systems (ANFIS) can be utilized during process development. In this article, we have therefore reviewed the recent application of ANN and ANFIS in modeling LCB valorization processes. Studies have shown the high predictive capability of both ANN and ANFIS for a range of different processes such as pre-treatment processes (microwave-assisted, organosolv-, ultrasound-assisted pre-treatment and many others), thermal processes (pyrolysis and gasification), enzymatic hydrolysis, and fermentation processes. These tools have also shown outstanding accuracy in predicting elemental composition and thermal characteristics of biomass by using only the proximate composition of LCB as the input information. In combination with evolutionary algorithms like genetic algorithm, particle swarm optimization or ant colony optimization, the ANN and ANFIS tools have shown excellent results in obtaining operational conditions for the efficient production of bioethanol, biogas, organic acids, lignin, and enzymes. However, there are only limited reports of the application of ANN and ANFIS in enzyme, organic acid and lignin production. Further research is therefore required to assess the suitability of using these tools in process development for the production of lignin, enzymes, and organic acids.

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Section: Application In Thermal Process Modelingmentioning

confidence: 99%

Artificial neural networks in valorization process modeling of lignocellulosic biomass

Pradhan

Jaiswal

2022

Biofuels Bioprod Bioref

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“…Proximate analysis is the most common analytical technique utilized for biomass characterization. It includes the identification of various fractions (1) volatile matter, the vapour resulting from biomass heating (2) fixed carbon, the char fraction that remains after de-volatilization (3) moisture and (4) ash content, which is the inorganic fraction remaining after complete oxidation of biomass (Okafor and Daramola 2020;Basu 2013;Dhyani and Bhaskar 2018). Various methods are used to determine each of the fractions, as shown in Table 2, while fixed carbon is usually calculated by difference.…”

Section: Proximate Ultimate and Heating Value Analysesmentioning

confidence: 99%

“…Various ratios derived from such results provide an opportunity for comparison between feedstocks. The results obtained through an ultimate analysis can also be utilized to compute biomass heating values (Okafor and Daramola 2020;Basu 2013;Dhyani and Bhaskar 2018).…”

Section: Proximate Ultimate and Heating Value Analysesmentioning

confidence: 99%

“…Quantitative composition analysis can be carried out using FTIR coupled with statistical modelling, such as principal component analysis or partial least squares, to determine lignocellulosic fractions. This is referred to as chemometric analysis (Okafor and Daramola 2020;Ferrer et al 2016;Xu et al 2013). Lignocellulosic fractions can also be obtained via wet chemical analytical methods.…”

Section: (2)mentioning

confidence: 99%

“…Furthermore, heavy metals such as cadmium, mercury and lead may be present, which are usually classified as toxic elements. Inductively coupled plasma mass spectrometry (ICP-MS), inductively coupled plasma atomic emission spectrometry (ICP-AES) and inductively coupled plasma optical emission spectrometry (ICP-OES) are common analytical techniques for inorganic metal analysis (Okafor and Daramola 2020;Dhyani and Bhaskar 2018). EDXS analysis can also be used to detect surface inorganic element composition.…”

Section: (2)mentioning

confidence: 99%

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Industrial biochar systems for atmospheric carbon removal: a review

et al. 2021

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In the context of climate change, there is an urgent need for rapid and efficient methods to capture and sequester carbon from the atmosphere. For instance, production, use and storage of biochar are highly carbon negative, resulting in an estimated sequestration of 0.3–2 Gt CO2 year−1 by 2050. Yet, biochar production requires more knowledge on feedstocks, thermochemical conversion and end applications. Herein, we review the design and development of biochar systems, and we investigate the carbon removal industry. Carbon removal efforts are currently promoted via the voluntary market. The major commercialized technologies for offering atmospheric carbon removal are forestation, direct air carbon capture utilization and storage, soil carbon sequestration, wooden building elements and biochar, with corresponding fees ranging from 10 to 895 GBP (British pounds) per ton CO2. Biochar fees range from 52 to 131 GBP per ton CO2, which indicates that biochar production is a realistic strategy that can be deployed at large scale. Carbon removal services via biochar are currently offered through robust marketplaces that require extensive certification, verification and monitoring, which adds an element of credibility and authenticity. Biochar eligibility is highly dependent on the type of feedstock utilized and processing conditions employed. Process optimization is imperative to produce an end product that meets application-specific requirements, environmental regulations and achieve ultimate stability for carbon sequestration purposes.

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