Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions

Zhu, Xinzhe; Li, Yinan; Wang, Xiaonan

doi:10.1016/j.biortech.2019.121527

Cited by 250 publications

(125 citation statements)

References 48 publications

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“…Moreover, real‐world time‐series data are quite skewed in their distribution which presents another challenge in the form of class imbalance. Least to mention, given the multitude of ML algorithms, the selection of the most appropriate one for a complex manufacturing process remains a challenge, as there is no universal rule that determines such selections, and multiple algorithms are often trained and tested before the one with best accuracy and generalization ability is selected . This limitation can be addressed through lifelong learning where the selected algorithm continuously trains and learns from historical data and evolves over a period of time.…”

Section: Cpps and Ml: The Digital And Technology Aspect Of Smart Manufacturingmentioning

confidence: 99%

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Suvarna

Büth

Hejny

et al. 2020

Advanced Intelligent Systems

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According to statistics provided by the UN in 2018, the global urban population rose from 0.75 billion in 1950 to 4.2 billion by 2018, a 5.5-fold increase in nearly seven decades. Currently, this 4.2 billion urban population accounts for 55% of the current global population, and given the trend, the global urban population is expected to touch 68% of total population by 2050. [1,2] To keep abreast with this growing shift toward urbanization, the last decade witnessed a multitude of global projects across the USA, EU, Asia Pacific, and Middle East regions to cater to the needs of a futuristic urban population through the concept of "smart cities." [3,4] Some of the popular programs include Smart Cities Initiatives by the USA, [5] EU H2020 Smart Cities and Communities project, [6] Smart Nation Program by Singapore, [7] Global Initiative for Resource Efficient Cities (GI-REC) by UN, [8] etc., to mention a few. Existing literature shows that there is no single or standardized definition to the concept of a smart city. [9] However, the central theme usually revolves around the application of information and digital technologies to provide intelligent and innovative solutions to the demanding requirements of an urban ecosystem including infrastructure, transport, healthcare, governance, and security. [2,10] The overall essence and motivation of a smart city is to facilitate the highest quality of life to its residents, while simultaneously optimizing the resources and energy efficiency, aiding the overall sustainability, social, and economic development of the city. Given the fact that the concept of smart cities is constantly evolving, devising a single template to describe the various characteristic features of a smart city can be a challenging task. [9] However, notable efforts have been made to determine and define a few salient features or indices of a smart city, published by research organizations as well as by consulting firms. [11] Both types of reports present commonalities in terms of indices, which are integral to smart cities and hence enable their acceptance with a degree of confidence. Some of the most notable and cited reports include Cities in Motion Index by IESE, [12] Smart Cities Ranking by Juniper Research, [13] Top 50 Smart City

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Section: Cpps and Ml: The Digital And Technology Aspect Of Smart Manufacturingmentioning

confidence: 99%

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Suvarna

Büth

Hejny

et al. 2020

Advanced Intelligent Systems

Self Cite

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“…Most studies have used biomass characterization data from different analytical tools such as proximate analysis, ultimate analysis, and lignocellulosic composition analysis, as shown in Table 2. The inclusion of different biomass components allows previous AI studies for using contribution analysis or sensitivity analysis to quantitatively investigate the impacts of biomass compositions that are challenging to be explored by traditional pyrolysis models and are often explored by experiments (Liao et al, 2019; Sunphorka et al, 2017; Xing, Wang, et al, 2019; Zhu et al, 2019).…”

Section: Applications Of Artificial Intelligence To Bioenergy Systemsmentioning

confidence: 99%

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Ming-yang

Yao

2021

GCB Bioenergy

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Bioenergy is widely considered a sustainable alternative to fossil fuels. However, large‐scale applications of biomass‐based energy products are limited due to challenges related to feedstock variability, conversion economics, and supply chain reliability. Artificial intelligence (AI), an emerging concept, has been applied to bioenergy systems in recent decades to address those challenges. This paper reviewed 164 articles published between 2005 and 2019 that applied different AI techniques to bioenergy systems. This review focuses on identifying the unique capabilities of various AI techniques in addressing bioenergy‐related research challenges and improving the performance of bioenergy systems. Specifically, we characterized AI studies by their input variables, output variables, AI techniques, dataset size, and performance. We examined AI applications throughout the life cycle of bioenergy systems. We identified four areas in which AI has been mostly applied, including (1) the prediction of biomass properties, (2) the prediction of process performance of biomass conversion, including different conversion pathways and technologies, (3) the prediction of biofuel properties and the performance of bioenergy end‐use systems, and (4) supply chain modeling and optimization. Based on the review, AI is particularly useful in generating data that are hard to be measured directly, improving traditional models of biomass conversion and biofuel end‐uses, and overcoming the challenges of traditional computing techniques for bioenergy supply chain design and optimization. For future research, efforts are needed to develop standardized and practical procedures for selecting AI techniques and determining training data samples, to enhance data collection, documentation, and sharing across bioenergy‐related areas, and to explore the potential of AI in supporting the sustainable development of bioenergy systems from holistic perspectives.

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“…It has been proved in our previous study that prediction ability improved when more relevant information was introduced into the developed models. 45 The performances of the best ANN model for each of the eight impact categories based on the molecular descriptors from AlvaDesc were shown in Fig. 5.…”

Section: Lca Prediction Models With Deep Learning Neural Networkmentioning

confidence: 99%

Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes

Zhu

Wang

2020

ACS Sustainable Chem. Eng.

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The production process of many active pharmaceutical ingredients such as sitagliptin could cause severe environmental problems due to the use of toxic chemical materials and production infrastructure, energy consumption and wastes treatment. The environmental impacts of sitagliptin production process were estimated with life cycle assessment (LCA) method, which suggested that the use of chemical materials provided the major environmental impacts. Both methods of Ecoindicator 99 and ReCiPe endpoints confirmed that chemical feedstock accounted 83% and 70% of life-cycle impact, respectively. Among all the chemical materials used in the sitagliptin production process, trifluoroacetic anhydride was identified as the largest influential factor in most impact categories according to the results of ReCiPe midpoints method. Therefore, high-throughput screening was performed to seek for green chemical substitutes to replace the target chemical (i.e. trifluoroacetic anhydride) by the following three steps. Firstly, thirty most similar chemicals were obtained from two million candidate alternatives in PubChem database based on their molecular descriptors. Thereafter, deep learning neural network models were developed to predict life-cycle impact according to the chemicals in Ecoinvent v3.5 database with known LCA values and corresponding molecular descriptors. Finally, 1,2-ethanediyl ester was proved to be one of the potential greener substitutes after the LCA data of these similar chemicals were predicted using the well-trained machine learning models. The case study demonstrated the applicability of the novel framework to screen green chemical substitutes and optimize the pharmaceutical manufacturing process.

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Machine learning prediction of biochar yield and carbon contents in biochar based on biomass characteristics and pyrolysis conditions

Cited by 250 publications

References 48 publications

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Smart Manufacturing for Smart Cities—Overview, Insights, and Future Directions

Applications of artificial intelligence‐based modeling for bioenergy systems: A review

Application of Life Cycle Assessment and Machine Learning for High-Throughput Screening of Green Chemical Substitutes

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