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For both investors and decision-makers, coking coal price estimates are essential due to the commodity’s importance as a tactical energy source. The present work uses a data-set of coking coal futures price indices traded on China Dalian Commodity Exchange from January 4, 2016 to December 31, 2020 in order to investigate the applicability of Gaussian process regressions for this forecast problem. There hasn’t been enough focus on price forecasting for this important financial indicator in the literature. Bayesian optimizations and cross-validation are used in our forecast model building processes. With an out-of-sample relative root mean square error of 1.3523%, the developed models accurately forecast the price from 01/02/2020 to 12/31/2020. It is demonstrated that Gaussian process regressions are helpful for the coking coal price forecast issue. It is possible to utilize the projection’s results alone as technical forecasts or in combination with other projections for policy research that involves forming opinions about price trends.

Coking wastewater poses a serious threat to the environment due to the presence of a wide spectrum of refractory substances such as phenolic compounds, polycyclic aromatic hydrocarbons and heterocyclic nitrogenous compounds. These toxic substances are difficult to treat using conventional treatment methods alone. In recent years much attention has been given to the effective treatment of coking wastewater. Thus, this review seeks to provide a brief overview of recent developments that have taken place in the treatment of coking wastewater. In addition, this article addresses the complexity and the problems associated with treatment followed by a discussion on biological methods with special focus on bioaugmentation. As coking wastewater is refractory in nature, some of the studies have been related to improving the biodegradability of wastewater. The final section focuses on the integrated treatment methods that have emerged as the best solution for tackling the highly unmanageable coking wastewater. Attention has also been given to emerging microwave technology which has tremendous potential for treatment of coking wastewater.

Large-scale carbon fixation requires high-volume chemicals production from carbon dioxide. Dry reforming of methane could provide an economically feasible route if coke- and sintering-resistant catalysts were developed. Here, we report a molybdenum-doped nickel nanocatalyst that is stabilized at the edges of a single-crystalline magnesium oxide (MgO) support and show quantitative production of synthesis gas from dry reforming of methane. The catalyst runs more than 850 hours of continuous operation under 60 liters per unit mass of catalyst per hour reactive gas flow with no detectable coking. Synchrotron studies also show no sintering and reveal that during activation, 2.9 nanometers as synthesized crystallites move to combine into stable 17-nanometer grains at the edges of MgO crystals above the Tammann temperature. Our findings enable an industrially and economically viable path for carbon reclamation, and the “Nanocatalysts On Single Crystal Edges” technique could lead to stable catalyst designs for many challenging reactions.

Coking and metal sintering are limitations of large-scale applications of Ni/Al2O3 catalysts in DRM reactions. In this review, several modification strategies to enhance the anti-deactivation property of Ni/Al2O3 are proposed and discussed with the recently developed catalyst systems, including structure and morphology control, surface acidity/basicity, interfacial engineering and oxygen defects. In addition, the structure–performance relationship and deactivation/anti-deactivation mechanisms are illustrated in depth, followed by prospects for future work.

Technological schemes of delayed coking plants currently operated in oil refineries are compared. It is shown that the new-generation Russian technology is superior to licensed technologies of foreign firms in operational reliability, efficiency, and potentiality of producing various types of coke.

The steel sector currently accounts for 7% of global energy-related CO 2 emissions and requires deep reform to disconnect from fossil fuels. Here, we investigate the market competitiveness of one of the widely considered decarbonisation routes for primary steel production: green hydrogen-based direct reduction of iron ore followed by electric arc furnace steelmaking. Through analysing over 300 locations by combined use of optimisation and machine learning, we show that competitive renewables-based steel production is located nearby the tropic of Capricorn and Cancer, characterised by superior solar with supplementary onshore wind, in addition to high-quality iron ore and low steelworker wages. If coking coal prices remain high, fossil-free steel could attain competitiveness in favourable locations from 2030, further improving towards 2050. Large-scale implementation requires attention to the abundance of suitable iron ore and other resources such as land and water, technical challenges associated with direct reduction, and future supply chain configuration. Facility-level analysis of green H2- based steel production demonstrates co-location of high-quality renewables and iron ore resources is imperative for cost minimisation.

The recent availability of shale gas has led to a renewed interest in C-H bond activation as the first step towards the synthesis of fuels and fine chemicals. Heterogeneous catalysts based on Ni and Pt can perform this chemistry, but deactivate easily due to coke formation. Cu-based catalysts are not practical due to high C-H activation barriers, but their weaker binding to adsorbates offers resilience to coking. Using Pt/Cu single-atom alloys (SAAs), we examine C-H activation in a number of systems including methyl groups, methane and butane using a combination of simulations, surface science and catalysis studies. We find that Pt/Cu SAAs activate C-H bonds more efficiently than Cu, are stable for days under realistic operating conditions, and avoid the problem of coking typically encountered with Pt. Pt/Cu SAAs therefore offer a new approach to coke-resistant C-H activation chemistry, with the added economic benefit that the precious metal is diluted at the atomic limit.

Optimizing coal blending is important for high-quality development of coking industries, among which deep understanding of relationships between coal characteristics and coke quality is critical. This work selected four typical coals from Shanxi Province in China to investigate influences of their structures and properties on coke quality. Although these samples belong to coking coals, the mechanical strength and thermal strength of the corresponding cokes are quite different. Macerals in coals, especially vitrinite, have significant effect on thermal strength of cokes. The thermal strength of coke B is better than coke A, because coal A mainly contains desmocollinite and coal B has more telocollinite. The CSR of coke B, C and D is higher than 60%, indicating they possess good thermal property. In the coking process, relatively low initial softening temperature (<400 °C), wide plastic temperature range (>100 °C), smooth fluidity region and appropriate maximum fluidity is helpful to improve coke quality based on Gieseler fluidity analysis. Coal C and Coal D have lower condensation degree, shorter aliphatic chain, and more hydrogen bond, which reveals that the condensation degree and hydrogen bond play important roles on the formation of plastic mass and coke thermal strength. Coke A shows unsatisfied properties because coal A has higher condensation degree and less hydrogen bond. In addition, TG-MS and CH4 evolution characteristics also imply the volatile matter released from coal A during pyrolysis mainly comes from the covalent bond with higher bond energy, which indicates that the chemical bond of coal A is more stable than other coals.

The combination of well-defined acid sites, shape-selective properties and outstanding stability places zeolites among the most practically relevant heterogeneous catalysts. The development of structure–performance descriptors for processes that they catalyse has been a matter of intense debate, both in industry and academia, and the direct conversion of methanol to olefins is a prototypical system in which various catalytic functions contribute to the overall performance. Propylene selectivity and resistance to coking are the two most important parameters in developing new methanol-to-olefin catalysts. Here, we present a systematic investigation on the effect of acidity on the performance of the zeolite ‘ZSM-5’ for the production of propylene. Our results demonstrate that the isolation of Brønsted acid sites is key to the selective formation of propylene. Also, the introduction of Lewis acid sites prevents the formation of coke, hence drastically increasing catalyst lifetime.

Coking coal price forecasting is a significant issue for investors and policy makers. This study explores usefulness of the nonlinear autoregressive neural network for this forecasting problem in a dataset of daily closing prices of the coking coal futures traded in China Dalian Commodity Exchange during January 4, 2016–December 31, 2020. Through examining various model settings across the algorithm, delay, hidden neuron, and data splitting ratio, the model leading to generally accurate and stable performance is reached. Particularly, the model’s inputs are the lagged coking coal futures prices and output is the 1-day ahead price forecast. The model is based on the two-layer feedforward network with six delays and two hidden neurons, which is trained through the Levenberg-Marquardt algorithm, and leads to relative root mean square errors of 1.84%, 1.85%, and 1.84% for the training, validation, and testing phases, respectively. Usefulness of the machine learning technique for the price forecasting problem of the coking coal price is illustrated. Results here might be used on a standalone basis as technical forecasts or combined with fundamental forecasts to form perspectives of price trends and perform policy analysis.