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Green hydrogen value chains in the industrial sector—Geopolitical and market implications
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Cross-sectoral assessment of CO2 capture from U.S. industrial flue gases for fuels and chemicals manufacture
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Corrigendum to “Cross-sectoral assessment of CO2 capture from U.S. industrial flue gases for fuels and chemicals manufacture” [International Journal of Greenhouse Gas Control 135 (2024) 1-20 / 104137]
The authors regret the inaccuracy in the vertical axis title of Fig. 4 and the distortion in the legend of Fig. 13. Corrections have been made to the vertical axis title and legend of Figs. 4 and 13, respectively. These changes do not affect the analysis, calculations, or results in any way. The authors apologize for any inconvenience caused. The corrected figures are as follows:
Variability in structural carbohydrates, lipid composition, and cellulosic sugar production from industrial hemp varieties
The aim of this study was to determine carbohydrate recovery from hemp for ethanol production and quantify biodiesel from TAG (Triacylglycerol) present in hemp. The structural composition of five different hemp varieties (Seward County-SC, York County-YC, Loup County-LC, 19 m96136-19 m, and CBD Hemp-CBD) were analyzed. Concentration of glucan and xylan ranged between 32.63 to 44.52% and 10.62 to 15.48% respectively. The biomass was then pretreated with Liquid hot water followed by disk milling and then hydrolyzed enzymatically to yield monomeric sugars. High glucose (63-85%) and xylose (73-88%) recovery was achieved. Lipids were extracted from hemp using hexane and isopropanol and then transesterified to produce biodiesel. Approximately, 50% of total fatty acids in SC, LC, and CBD hemp were linoleic acid. Palmitic acid was present between 32 to 50% in varieties YC and 19 m. Highest TAG concentration at 25% of total lipids was observed in CBD hemp. In conclusion, the analysis on lipid composition and high sugar recovery demonstrates hemp as a potential bioenergy crop for ethanol and biodiesel coproduction.
China's vehicle electrification impacts on sales, fuel use, and battery material demand through 2050: Optimizing consumer and industry decisions
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Road to zero: Research and industry perspectives on zero-emission commercial vehicles
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Real-time optimization of multi-cell industrial evaporative cooling towers using machine learning and particle swarm optimization
Existing electrical generating stations must operate with greater flexibility due to increasing renewable energy penetration on the electrical grid, and many coal-fired power stations have transitioned away from baseload operation to load-following operation to aid in grid stability. In cases where multiple independently controlled cooling tower cells are used in parallel for the cooling purposes of such stations, there is an opportunity to increase plant efficiency through data-driven optimization across their full load ranges. This work presents a novel application of real-time optimization using machine learning and particle swarm optimization on a multi-cell induced-draft cooling tower servicing a coal-fired power station under variable load. This is the first work to demonstrate simultaneous optimization of a multi-cell cooling tower, in addition to using machine learning for closed-loop control on a cooling tower. A novel control configuration is presented that ensures original control logic is not adversely affected and that the overall plant process is not disrupted using only existing hardware and operational data. To verify this methodology, the 12 independent cooling tower cells are simulated in parallel using historic operating data to demonstrate the effectiveness of real-time optimization compared to current practice. An artificial neural network is trained to predict overall cooling tower power consumption using only operational data and ambient conditions with an R2 value of greater than 0.96. The real-time optimization using particle swarm yields 6.7% annual energy usage savings compared to current practices, although the extent of the real-time savings varies greatly with both plant load and environmental conditions. This is particularly significant for a variable load situation because frequent ramping typically results in reduced overall efficiency. Furthermore, this proposed AI-based solution presents an opportunity to improve the overall heat rate of a load-following coal-fired power plant without the need to perform extensive first-principles modeling or add additional hardware to the cooling tower, resulting in more resources conserved and less overall emissions per unit of electricity generated.
A data engineering framework for on-site end-of-life industrial operations
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Process intensification and comparison of electrolytic hydrogen green steel plants for industrial decarbonization
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Rapid industrial scale synthesis of robust carbon nanotube network electrodes for electroanalysis
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