King Mongkut's University of Technology North Bangkok

The chapter presents the results of theoretical analysis and practical implementation of small blast furnaces operation using a charge of ore-carbon agglomerated products (briquettes and unfired pellets on a cement binder). It is shown that the use of such charge materials in the blast furnace charge leads to a decrease in the total fuel consumption and a proportional decrease in CO2 emissions. Particular attention is paid to modeling the kinetics of carbothermic processes in a blast furnace based on mathematical modeling and simplified methods of generalized calculation.

The Chapter gives the characteristics of small blast furnaces and the comparison of large and small blast furnaces, estimates the number of furnaces in the world that can be classified as small, and the distribution by the main operating countries. A brief overview of the development of small furnaces in Europe, Brazil, Russia and countries of the former Soviet Union, China, India and ASEAN is given, taking into account regional characteristics, including the main process fuel, charge materials, the target product as well as the challenges facing blast furnaces and shaft recycling units. A preliminary conclusion has been made on the prospects for using mini blast furnaces in different regions. A case study of a mini blast furnace operating on 100% briquette charge is described.

The Chapter describes possible substitutes for coke and methods of its use in blast furnace production and, in particular, in small blast furnaces. It is shown that for MBFs it is most effective to use in significant quantities such coke’s substitutes as biomass of various origins, plastic waste, briquettes from coke breeze and dust, as well as ore-carbon briquettes. Blast furnace can be an effective unit for recycling plastic waste, including the most dangerous ones, as coke substitute. The substitutes mentioned can be used both in the form of a charge with iron ore part or with coke, and blown through tuyeres. Also for MBF, an option is offered to divide coke and its substitutes for two parts: one part is of the highest possible quality for forming coke packing, the second is low-quality coke and its substitutes to ensure the recovery of the charge and combustion on tuyeres.

The Chapter examines the features of operation of small blast furnaces on coke and charcoal, theoretical and practical aspects of design and operation of small blast furnaces. Due to the lack of literary data on the operation of small blast furnaces for smelting ferroalloys, the Chapter provides a general description of the state of affairs in this branch of metallurgy and details the theoretical and practical aspects of smelting ferromanganese by small blast furnaces. An overview of known implemented projects in the world is provided.

Despite the unprecedented tightening of requirements for radical decarbonization of the modern ferrous metallurgy, iron smelting in blast furnaces continues to remain a stable dominant extraction process amid growing disappointment with the initial results of decarbonization. The cost of steelmaking in the maximum decarbonization scenario using hydrogen produced by electrolysis using renewable energy sources would make the ferrous metallurgy unprofitable.

The Chapter provides a brief description of the development of metallurgy from the bloomery the blast furnace, provides indicators of furnace productivity growth to the mid-twentieth century, and the main milestones in the development of pig iron production—coke as the main technological fuel and blast heating. It is shown that until the mid-twentieth century, the productivity of none of the “large” blast furnaces exceeded the production level of a modern compact or even mini-blast furnace.

We are mainly concerned with a perturbed hemivariational inequality involving time-dependent maximal monotone operators and history-dependent operators. First, we prove the existence results for the problem. Next, we establish the density and co-density results of the solution set. Finally, as an illustrative application, we study a frictional contact problem for viscoelastic materials.

The objective of this research work is to investigate the influence of recycled amethyst on the properties of poly(vinylidene fluoride‐ co ‐hexafluoropropylene) (PVDF‐HFP) composite films, with a particular focus on enhancing mechanical and electrical properties for energy harvesting applications. The composite films made from PVDF‐HFP/waste amethyst were fabricated by varying the amethyst content (0 to 0.5 parts per hundred of resin, phr) and using amethyst particles with an average size of 9.02 μm, along with a fixed 3% silane coupling agent (Silane‐69). X‐ray fluorescence (XRF) analysis revealed that amethyst is primarily composed of SiO₂ with trace amounts of iron. Increasing the amethyst content resulted in notable improvements in mechanical performance, with ultimate tensile strength increasing from 4.75 to 7.43 MPa and Young's modulus rising from 156.00 to 258.21 MPa. Water contact angle measurements demonstrated enhanced hydrophobicity, increasing from 66° to 73°, while X‐ray diffraction (XRD) showed an increase in β‐phase crystallinity, from 0.539 to 0.600. SEM–EDX mapping confirmed the uniform dispersion of amethyst particles and an increase in surface roughness. Electrical testing revealed that the composite with 0.5 phr amethyst exhibited the highest conductivity and dielectric constant. This study highlights the potential of using wasted amethyst as an eco‐friendly additive to improve the dielectric properties of PVDF‐HFP composite films. This innovative approach not only enhances dielectric performance but also promotes sustainability by utilizing wasted materials, offering advantages in both cost‐effectiveness and environmental impact. Highlights Waste amethyst is used as an effective additive for sustainable applications. Waste amethyst improves the dielectric and mechanical properties of PVDF‐HFP. Waste amethyst increases the β‐phase of PVDF‐HFP. Waste amethyst enhances the conductivity of PVDF‐HFP. PVDF‐HFP composite shows the potential for sustainable energy materials.

Composites materials reinforced with natural fibers are currently gaining traction in many industries including automotive, aerospace, marine, packaging and construction due to their ecological consciousness and high strength to weight ratio. To enhance the overall performance and use of natural fibers composites (NFC) in different industries, it is crucial to understand their acoustic properties, moisture absorption, mechanical characteristics, manufacturing processes, tribological behavior and damage mechanics. Analyzing the performance of NFC is a complex process due to the heterogeneity and anisotropic nature of NFC coupled with their susceptibility to environmental factors that lead to a significant variability in their composites. Research on NFC performance typically depends on the time consuming and costly experiments with limited reproducibility and computationally intensive simulations. Machine learning (ML) techniques can efficiently uncover data patterns and offer high reproducibility. Additionally, advancements in NFC manufacturing and testing have produced vast amounts of data. The current review not only discusses the application of ML methods in enhancing NFC performance, but also identifies the challenges and opportunities associated with using ML in NFC research. By utilizing ML methods, NFC limitations can be overcome, leading to improved performance.

Four new oxepin and dihydrobenzofuran derivatives, saccoxepins A–C (1–3) and saccobenzofurin A (4), along with one known compound, bauhinoxepin A (5), were isolated from the roots of Bauhinia saccocalyx. The structures were elucidated by extensive analysis of spectroscopic data in combination with ECD analysis. The EtOAc extract exhibited significant NO inhibition (94.4 ± 0.35%, 50 μg/mL), and saccoxepin A and bauhinoxepin A demonstrated strong NO suppression, with IC50 values of 49.35 µM and 30.28 µM, respectively, alongside notable antioxidant activity. Saccoxepin A and bauhinoxepin A selectively reduced interleukin-6 (IL-6) levels, while bauhinoxepin A slightly lowered tumor necrosis factor-alpha (TNF-α) at a low dose. Furthermore, bauhinoxepin A exhibited cytotoxicity against HCT-116 cells, with an IC50 of 8.88 µM. These findings suggest that the roots of B. saccocalyx possess potent antioxidant, anti-inflammatory, and anticancer activities, supporting its traditional medicinal applications and highlighting its potential as a source of therapeutic agents.

The aim of this study is to examine circular tunnel stability by investigating the influence of the spatial variability of rock masses by using the Hoek–Brown model, random field theory, and random adaptive finite element limit analysis (RAFELA). The analysis investigates the influences of six input parameters, including the cover depth ratio (C/D), the geological strength index dependent on rock quality (GSI), the yield parameter (mi), the mean of variation (COVσci), the dimensionless correlation length (Θ), and the specified factor of safety (FoS), on the probabilistic analysis of circular tunnel stability. The mean stability number μNran increases, and the PoF decreases with increasing correlation length. The failure patterns of circular tunnels in rock under deterministic analysis and stochastic analysis have been examined. Furthermore, the eXtreme Gradient Boosting (XGB) method was selected to consider the relationships between the investigated parameters and the stability factor. A hybrid machine-learning framework is proposed that integrates an optimization algorithm, namely, particle swarm optimization (PSO), into the XGB algorithm to increase its performance. The efficiency of the PSO-XGB model is suggested as the best hybrid XGB model (R² = 99.61%) for predicting the failure probability of a circular tunnel.

A low signal relative to background noise (signal-to-noise ratio: SNR) signifies a challenge in distinguishing the signal from the background noise. Herein, estimates for the simultaneous confidence intervals (SCIs) for the differences between the SNRs of several log-normal (LN) distributions are presented. These are based on the fiducial generalized confidence interval (FGCI), large sample (LS), method of variance estimates recovery (MOVER), and Bayesian (BS) approaches. By using a Monte Carlo simulation study with RStudio programming, all SCIs are compared based on their coverage probabilities and average lengths. The results indicate that LS approach provided shorter average lengths compared to the MOVER approach, the former approach is the most effective for estimating the SCIs for the differences among the SNRs of several LN distributions. Furthermore, these methods were also used to compare the equality of three price-earning ratios: the SET50, SET100, and sSET indexes. In conclusion, the LS approach proved to be the best method and is thus recommended for estimating the SCIs for the differences between the SNRs of multiple LN distributions.

The exponentially weighted moving average (EWMA) control chart is frequently employed to monitor changes in process parameters. We developed a method to efficiently track minor changes sensitively, particularly when the data of the process are correlated. The average run length (ARL) is an essential metric employed to evaluate the efficacy of a control chart. Herein we provide exact formulas for the in-control ARL (ARL0) and out-of-control ARL (ARL1) for the mean of a long-memory seasonal fractionally integrated moving average with an exogenous variable model order ( ) process with exponential white noise on an EWMA control chart. The ARL results obtained using the exact formulas method were consistent with those using the classical numerical integral equation method. The sensitivity of the EWMA control chart to changes in the ARL of the mean of a process using the proposed and NIE methods with a low ARL1 value and various change levels was assessed in terms of the percentage difference in the expected ARL obtained using both methods, while the standard deviation of the RL (SDRL) was employed to assess the detected changes. Furthermore, the performances of the methods were evaluated temporally. In contrast, NIE also takes the time to display ARL1 results in seconds. The extensive simulation-based results indicate that the exact formulas approach performed better than the NIE method for all change levels in the mean of the process in terms of the results delivery time. An illustrative monitoring example using data on electricity production from natural gas is also provided to demonstrate the proposed method's practicability.

The AdaMax algorithm provides enhanced convergence properties for stochastic optimization problems. In this paper, we present a regret bound for the AdaMax algorithm, offering a tighter and more refined analysis compared to existing bounds. This theoretical advancement provides deeper insights into the optimization landscape of machine learning algorithms. Specifically, the You Only Look Once (YOLO) framework has become well‐known as an extremely effective object segmentation tool, mostly because of its extraordinary accuracy in real‐time processing, which makes it a preferred option for many computer vision applications. Finally, we used this algorithm for image segmentation.

This work investigates the failure envelope of a rectangular skirt foundation in non-homogeneous clays based on the new novel soft-computing approach that combines FELA simulation and tree machine learning models, including decision tree (DT), random forest (RF), and gradient boosting (GB) models. The impacts of embedment depth (D/B), shape ratio (L/B), and soil heterogeneity (κ) that affect the failure envelope of the rectangular skirt foundation under combined loading conditions, including the vertical load (V), horizontal load (H), and moment (M), are investigated. Furthermore, decision trees, random forests, and gradient boosting are selected to consider the relationships between the investigated parameters and the failure envelope capacity (V/Asu, H/Asu, M/ALsu). To facilitate practical application, the numerical findings are given in the form of design charts and tables. The efficiency of the tree models is determined through regression parameters (i.e., R², RMSE, MSE, and MAE) combined with a Taylor chart. As a result, the decision tree model is suggested as the best model (R² = 0.999) for predicting the failure envelope of rectangular skirt foundations. Additionally, the failure mechanism of rectangular skirt footing in heterogeneous clay under combined loading (V, H, M) has been examined, enhancing the design of engineers in practice.