Chemical Engineering Research Articles

Preface to the special issue of the International Conference on Sustainable Development in Chemical and Environmental Engineering (SDCEE‐2024)

Preface to the special issue of the International Conference on Sustainable Development in Chemical and Environmental Engineering (<scp>SDCEE</scp>‐2024)

Photoredox phase engineering of transition metal dichalcogenides

Crystallographic phase engineering plays an important part in the precise control of the physical and electronic properties of materials. In two-dimensional transition metal dichalcogenides (2D TMDs), phase engineering using chemical lithiation with the organometallization agent n-butyllithium (n-BuLi), to convert the semiconducting 2H (trigonal) to the metallic 1T (octahedral) phase, has been widely explored for applications in areas such as transistors, catalysis and batteries1–15. Although this chemical phase engineering can be performed at ambient temperatures and pressures, the underlying mechanisms are poorly understood, and the use of n-BuLi raises notable safety concerns. Here we optically visualize the archetypical phase transition from the 2H to the 1T phase in mono- and bilayer 2D TMDs and discover that this reaction can be accelerated by up to six orders of magnitude using low-power illumination at 455 nm. We identify that the above-gap illumination improves the rate-limiting charge-transfer kinetics through a photoredox process. We use this method to achieve rapid and high-quality phase engineering of TMDs and demonstrate that this methodology can be harnessed to inscribe arbitrary phase patterns with diffraction-limited edge resolution into few-layer TMDs. Finally, we replace pyrophoric n-BuLi with safer polycyclic aromatic organolithiation agents and show that their performance exceeds that of n-BuLi as a phase transition agent. Our work opens opportunities for exploring the in situ characterization of electrochemical processes and paves the way for sustainably scaling up materials and devices by photoredox phase engineering.

Radiative heat recuperation in combustion of layered solid fuel

In this work, we investigate the properties and stability of the combustion waves propagating in a system of layered solid fuel material in which the neighbouring fuel sheets can recuperate heat via thermal electromagnetic radiation. The analysis is undertaken within the flame sheet model, which allows to determine the combustion wave speed and structure and derive the dispersion relation characterising the stability of the reaction front. The proper choice of parameters leads to superadiabatic combustion temperature and significant increase of the combustion wave speed. In addition, the stable steady regimes of combustion can be substantially extended in the space of parameters. Such an enhancement of stability and ability to control the characteristics of the combustion wave propagation can be very attractive for the combustion synthesis of new layered materials, similarly to the chemical furnace technology proposed earlier.

Stability and Convergence Analysis of the Discrete Dynamical System for Simulating a Moving Bed

The efficiency of controlling the simulated moving bed (SMB) has long been a critical issue in the chemical engineering industry. Most existing research relies on finite element methods, which often result in lower control efficiency and are unable to achieve online control. To enhance control over the SMB process, this paper employs the Crank–Nicolson method to develop a discrete dynamical model. This approach allows for the investigation of system stability and convergence, fundamentally addressing the sources of error. During the discretization of partial differential equations (PDEs), two main types of errors arise: intrinsic errors from the method itself and truncation errors due to derivative approximations and the discretization process. Research indicates that for the former, the iterative process remains convergent as long as the time and spatial steps are sufficiently small. Regarding truncation errors, studies have demonstrated that they exhibit second-order behavior relative to time and spatial steps. The theoretical validation shows that the iteration works effectively, and simulations confirm that the finite difference method is stable and performs well with varying SMB system parameters and controller processes. This provides a solid theoretical foundation for practical, real-time online control.

Paracetamol sensing with a flexible graphene paper electrode modified with MnCo2O4

AbstractBACKGROUNDThe design of new‐generation flexible sensors for medical applications has gained considerable interest in the research community in recent past years. Herein, a free‐standing flexible electrode was developed for the electrochemical determination of Paracetamol (PC). This graphene‐based paper electrode was prepared using graphene and MnCo2O4 dispersion with a mold‐casting method followed by chemical reduction. The morphology and structure of our flexible paper electrode (MnCo2O4/GP) were characterized by scanning electron microscopy, energy‐dispersive, X‐ray diffraction, X‐ray photoelectron, and Raman spectroscopy.RESULTSThe as‐prepared flexible MnCo2O4/GP showed outstanding sensing performance for PC, as compared to GP, due to the excellent electrochemical properties of MnCo2O4. The proposed sensor exhibited a wide linear range of 4.64–1000 μM for electrochemical determination of PC with a low detection limit of 1.39 μM. Furthermore, MnCo2O4/GP was successfully applied for PC syrup and tablets with good accuracy results.CONCLUSIONSFor the first time, the present study demonstrated the fabrication and utilization of MnCo2O4‐modified graphene paper to develop a flexible and sensitive electrode for monitoring PC. © 2024 The Author(s). Journal of Chemical Technology and Biotechnology published by John Wiley &amp; Sons Ltd on behalf of Society of Chemical Industry (SCI).

Direct propylene epoxidation with molecular oxygen over titanosilicate zeolites.

The direct epoxidation of propylene with molecular oxygen represents a desired route for propylene oxide (PO) production with 100% theoretical atomic economy. However, this aerobic epoxidation reaction suffers from the apparent trade-off between propylene conversion and PO selectivity, and remains a key challenge in catalysis. We report that Ti-Beta zeolites containing isolated framework Ti species can efficiently catalyze the aerobic epoxidation of propylene. Stable propylene conversion of 25% and PO selectivity of up to 90% are achieved at the same time, matching the levels of industrial ethylene aerobic epoxidation processes. H-terminated pentacoordinated Ti species in Beta zeolite frameworks are identified as the preferred active sites for propylene aerobic epoxidation and the reaction is initiated by the participation of lattice oxygen in Ti-OH. These results are expected to spark new technology for the industrial production of PO toward more sustainable chemistry and chemical engineering.

Energetic Hypervalent Organoiodine Electrochemistry for Aqueous Zinc Batteries.

Hypervalent organoiodine compounds have been extensively utilized in organic synthesis, yet their electrochemical properties remain unexplored despite their theoretically high redox potential compared with inorganic iodine, which primarily relies on the I-/I0 redox couple in battery applications. Here, the fundamental redox mechanism of hypervalent organoiodine in a ZnCl2 aqueous electrolyte is established for the first time using the simplest iodobenzene (PhI) as a model compound. We validated that the PhI to PhICl2 transition is a single-step and reversible reaction, enabling two-electron transfer of I+/I3+ redox chemistry (1.9 V vs Zn2+/Zn) with high capacity (422 mAh giodine-1, and 262.6 mAh g-1 based on PhI) and high theoretical energy density (801.8 Wh kg-1). It was also elucidated that such organoiodine electrochemistry exhibits rich tunability in terms of the global reactivity of various PhI derivatives, including multiple iodine-substituted isomers and functional substituents. Additionally, the stabilizing anion ligands affect the reversibility and stability of trivalent organoiodine compounds. By limiting side reactions and improving the stability of trivalent organoiodine at low temperatures, the zinc-PhI battery demonstrated the feasibility of I+/I3+ conversion and sustained stable performance over 400 cycles. This work bridges the gap between hypervalent organoiodine chemistry and battery technology, highlighting the potential for future high-performance battery applications.

Artificial Neural Network-driven Optimization of Fe3O4 Nanoparticles/PVDF Macrospheres in Fenton-like System for Methylene Blue Degradation

Efficient degradation of industrial dyes remains a critical challenge in environmental engineering. This study introduces a novel Fe3O4 nanoparticles/PVDF macrospheres in a Fenton-like system, optimized using an Artificial Neural Network (ANN) for the degradation of Methylene Blue (MB). A feedforward backpropagation neural network model to optimize and predict the performance of this advanced oxidation process under various operational conditions. The model was trained, validated, and tested with robust datasets, demonstrating high predictive accuracy and generalization capability. The Mean Square Error (MSE) and Root Mean Square Error (RMSE) during testing were 0.0200 and 0.1414, respectively, indicating precise predictions. The coefficient of determination (R²) and correlation coefficient (R) were exceptionally high at 0.9744 and 0.9871, affirming the model's ability to capture the underlying dynamics of the degradation process effectively. The ANN-driven approach not only enhanced the efficiency of the MB degradation process but also provided significant insights into the scalability and applicability of the Fe3O4/PVDF system for practical water treatment solutions. This study underscores the potential of integrating advanced machine learning techniques with chemical engineering processes to achieve sustainable and efficient environmental management solutions, particularly for the treatment of recalcitrant wastewater contaminants.

Analysis of the Impact of Innovation Subsidy Policy on Biological Enterprises

BASF, one of the world's leading chemical enterprises, has been actively engaged in biotechnology in recent years, committed to integrating biotechnology with traditional chemical technology and promoting the development of sustainable solutions. This report aims to analyze BASF's financial statements for 2021, 2022, and others to evaluate the performance of its biotechnology business in four key aspects: solvency, upstream and downstream competition, profitability, and operating capacity, to provide a reference for corporate decision-makers and investors. According to the public information, BASF is financially stable, and its ROCE has increased significantly, reaching 13.5% in 2021, far higher than the cost of capital, which shows the company's strong profitability and good solvency. BASF's biotechnology business has performed well in profitability. The bio-solution business contributes more than 150 million euros in annual sales and continues to grow. BASF's biological control products, seeds and traits business have been registered or are being registered in many countries, showing strong market potential and profitability. BASF also performed well in the operational capability of biotechnology business. The company has established lean corporate headquarters and global business service department to provide efficient and demand-oriented internal services. With the intensification of market competition and the continuous progress of technology, BASF needs to continue to increase R&amp;D investment, expand market share, optimize supply chain management and improve operational efficiency in order to maintain its leading position and achieve sustainable development.

Analysis of Interdisciplinary Thematic Integrated Curriculum Focusing on Core Competencies

To address the complex societal demands of the globalized and information age, this paper adopts an interdisciplinary thematic teaching approach to study an integrated curriculum in higher vocational education centered around the theme of mobile phone battery charging time. The selected themes are related to students cognitive development, interest exploration, and real-life experiences, integrating knowledge from disciplines such as mathematics, physics, chemistry, and information technology, and designing student-centered teaching activities. The study shows that interdisciplinary thematic teaching not only enhances students mathematical application skills and problem-solving abilities but also effectively cultivates their multidisciplinary competencies, improving the efficacy and engagement of education.

Constructing an artificial in vitro multi-enzyme cascade pathway to convert glycerol and CO2 into L-aspartic acid

Developing utilization technologies for biomass resources, exploring their applications in the fields of energy and chemical engineering, holds significant importance for promoting sustainable development and constructing a green, low-carbon society. In this study, we designed a non-natural in vitro multi-enzyme system for converting glycerol and CO2 into L-aspartic acid (L-Asp). The coupled system utilized eight enzymes, including alditol oxidase (ALDO), catalase-peroxidase (CAT), lactaldehyde dehydrogenase (ALDH), glycerate 2-kinase (GK), phosphopyruvate hydratase (PPH), phosphoenolpyruvate carboxylase (PPC), L-aspartate dehydrogenase (ASPD), and polyphosphate kinase (PPK), to convert the raw materials into L-Asp in one-pot coupled with NADH and ATP regeneration. Under optimal reaction conditions, 18.6 mM of L-Asp could be produced within 2.0 h at a total enzyme addition of 4.85 mg/mL, demonstrating the high efficiency and productivity characteristics of the designed system. Our technological application provides new insights and methods for the development of biomass resource utilization technologies.

Bubble behavior parameters extraction and analysis during pool boiling based on deep-learning method

The nucleate pool boiling plays an important role in thermal and chemical engineering applications. Analyzing bubble dynamics at nucleation site is crucial to improve the understanding of boiling heat transfer mechanism. Quantitative extraction of bubble parameters from high-speed visualized images is a labor-intensitive and time-consuming task making it necessary for automatically detect single bubble growth and measure boiling characteristic parameters.In the present work, we proposed a deep learning based self-adaptive statistical algorithm for extraction of bubble behavior parameters quickly and automatically from numerous high-speed visualization images looking from the side view of a boiling chamber. A dataset was constructed for training and performance evaluation based on experimental data of saline solution pool boiling. The StarDist and U-Net convolutional neural network were combined in the algorithm so that more exact segmentation of the bubbles can be identified. Based on the segmentation results, a post-processing program was developed to extract the sequential variation of bubbles during consecutive cycles at nucleation sites. The dynamic characteristic parameters that affect heat transfer, such as nucleation density, bubble departure diameter, departure frequency, and wait time under different heat flux were obtained quantitatively. The comparison of automatic extraction algorithm and manual processing proves the reliability and superiority of our method. This work indicates that the proposed method has great potential to be widely applied as an efficient and universal tool for processing different types of bubble shadowgraph images.

Removal of Heavy Metals from Wastewaters and Other Aqueous Streams by Pressure-Driven Membrane Technologies: An Outlook on Reverse Osmosis, Nanofiltration, Ultrafiltration and Microfiltration Potential from a Bibliometric Analysis.

A bibliometric study to analyze the scientific documents released until 2024 in the database Scopus related to the use of pressure-driven membrane technologies (microfiltration, ultrafiltration, nanofiltration and reverse osmosis) for heavy metal removal was conducted. The work aimed to assess the primary quantitative attributes of the research in this field during the specified period. A total of 2205 documents were identified, and the corresponding analysis indicated an exponential growth in the number of publications over time. The contribution of the three most productive countries (China, India and USA) accounts for more than 47.1% of the total number of publications, with Chinese institutions appearing as the most productive ones. Environmental Science was the most frequent knowledge category (51.9% contribution), followed by Chemistry and Chemical Engineering. The relative frequency of the keywords and a complete bibliometric network analysis allowed the conclusion that the low-pressure technologies (microfiltration and ultrafiltration) have been more deeply investigated than the high-pressure technologies (nanofiltration and reverse osmosis). Although porous low-pressure membranes are not adequate for the removal of dissolved heavy metals in ionic forms, the incorporation of embedded adsorbents within the membrane structure and the use of auxiliary chemicals to form metallic complexes or micelles that can be retained by this type of membrane are promising approaches. High-pressure membranes can achieve rejection percentages above 90% (99% in the case of reverse osmosis), but they imply lower permeate productivity and higher costs due to the required pressure gradients.

Enhancing Magnetic Micro- and Nanoparticle Separation with a Cost-Effective Microfluidic Device Fabricated by Laser Ablation of PMMA.

Superparamagnetic iron oxide micro- and nanoparticles have significant applications in biomedical and chemical engineering. This study presents the development and evaluation of a novel low-cost microfluidic device for the purification and hyperconcentration of these magnetic particles. The device, fabricated using laser ablation of polymethyl methacrylate (PMMA), leverages precise control over fluid dynamics to efficiently separate magnetic particles from non-magnetic ones. We assessed the device's performance through Multiphysics simulations and empirical tests, focusing on the separation of magnetite nanoparticles from blue carbon dots and magnetite microparticles from polystyrene microparticles at various total flow rates (TFRs). For nanoparticle separation, the device achieved a recall of up to 93.3 ± 4% and a precision of 95.9 ± 1.2% at an optimal TFR of 2 mL/h, significantly outperforming previous models, which only achieved a 50% recall. Microparticle separation demonstrated an accuracy of 98.1 ± 1% at a TFR of 2 mL/h in both simulations and experimental conditions. The Lagrangian model effectively captured the dynamics of magnetite microparticle separation from polystyrene microparticles, with close agreement between simulated and experimental results. Our findings underscore the device's robust capability in distinguishing between magnetic and non-magnetic particles at both micro- and nanoscales. This study highlights the potential of low-cost, non-cleanroom manufacturing techniques to produce high-performance microfluidic devices, thereby expanding their accessibility and applicability in various industrial and research settings. The integration of a continuous magnet, as opposed to segmented magnets in previous designs, was identified as a key factor in enhancing magnetic separation efficiency.

Recognizing the Most Accurate Herschel-Bulkley Fluid Pressure Estimation Method for Annulus: An Update to API RP 13D, 7th Edition

Summary The Herschel-Bulkley (HB) model is widely regarded as highly accurate for modeling non-Newtonian fluid flow, applicable across diverse fields such as chemical, mechanical, and petroleum engineering. The American Petroleum Institute and other references recognize the HB model as the most accurate and recommend the industry use it for drilling hydraulics. Pressure estimation models are beneficial for applying the HB model in field settings, as numerical solutions may not be feasible due to the computational demands and time constraints, especially challenging for high-frequency real-time pressure loss estimation (at least 1 Hz). Past literature has presented four estimation methods for the HB fluid, consisting of Zamora and Lord (1974), Merlo et al. (1995), Gjerstad and Time (2015), and the generalized method. However, the recommended practice API RP 13D (2023), “Rheology and Hydraulics of Oilwell Drilling Fluids,” has considered the most traditional Zamora and Lord (1974) as their chosen estimation method for industry use while neglecting to identify the most accurate model. Therefore, to update API RP 13D (2023), this work addresses this shortcoming by investigating the performance of different methods in estimating pressure losses while providing user-friendly, straightforward procedures applicable for field use. To recognize the most accurate estimation method for industry use, different methods’ pressure losses were estimated and compared with the numerical solution as well as experimental data to find the errors. The results showed that the Merlo et al. (1995) method provides the most accurate estimates, with the least average absolute percent error (APE) of maximum 4% underestimating the numerical solution, followed by Zamora and Lord’s error overestimating the numerical solution. The errors may slightly vary depending on the input parameters used, including diameters, mud properties, and circulation rate. The effect of the circulation rate on the accuracy of different methods was also investigated; except for the generalized method, the accuracy of the other methods increases with increasing the circulation rate. This work is a very innovative practical work for finding annular pressure losses accurately without using complex numerical solutions.

Biorefinery superstructure optimization under carbon pricing policies using stochastic programming

The growing utilization of biomass feedstocks for climate change mitigation has led to increased research on biorefineries. Concurrently, environmental policies are evolving as crucial tools to address these global concerns. Cap-and-trade, carbon tax, and carbon cap policies have been established to reduce carbon dioxide emissions. In this work, a mathematical optimization model is developed to integrate biorefinery process design with carbon pricing policies and crediting mechanisms. A two-stage stochastic mixed integer linear programming model is proposed to account for emissions, feedstock supply, chemical demand, and pricing uncertainties. A bi-objective optimization framework is employed to consider economic and environmental metrics. The framework is a valuable tool for governments and businesses to determine pareto-optimal investment strategies under environmental policies. It is applicable for evaluating prospective chemical technologies with carbon pricing policies beyond biorefineries. The results indicate that carbon crediting mechanisms can minimize the financial penalty by up to 50% under a carbon tax policy. Implementing chemical demand constraints within a cap-and-trade policy reduces potential profits, especially when the carbon prices are high. The stochastic programming approach revealed that underestimating the expected carbon cap leads to lower expected profits. Despite the financial implications of these policies, profitable process designs are achievable.

Numerical investigation of the physical-thermal-chemical behaviours of particles during coal combustion and desulfurization processes in a CFB combustor

The desulfurization process has been practised for the clean utilization of coal in chemical engineering fields, yet the physical, thermal, and chemical characteristics of particles are still lacking understanding. In this work, a comprehensive numerical model based on the Eulerian-Lagrangian framework was established to study the particle behaviours during coal combustion and desulfurization processes in a pilot-scale circulating fluidized bed (CFB) combustor. After model validation, the effects of several crucial operation parameters (e.g., excess air ratio, calcium to sulphur ratio, and calcium oxide size) on particle behaviours are illuminated. The results show that density-induced segregation causes coal particles to accumulate in the upper part of the riser. The sand, coal, and CaO particles have time-averaged Reynolds numbers of 15, 6.5, and 0.5, respectively. The particle temperature is higher in areas with lower solid concentrations. Increasing excess air ratio (ϕg) decreases the temperature of sand particles but elevates that of coal and CaO particles. The average particle heat transfer coefficients (HTCs) for sand, coal, and CaO particles are 205 W/m2·K, 172 W/m2·K, and 275 W/m2·K, respectively. With the increase in ϕg, the HTC of sand particles increases but that of CaO particles remains unchanged, additionally, the mass of coal particles decreases and the mass of carbon in coal particles decreases along with the riser. The influence of the Ca/S ratio and CaO size on the axial distribution of coal particle mass and carbon mass fraction is negligible. For each particle species, the axial dispersion coefficient (Dz) is two orders of magnitude greater than the radial ones (Dx, Dy), indicating that the vertical introduction of the fluidizing gas dominates the dense gas-solid flow in the riser.

Textile waste pretreatment for anaerobic digestion: a review and technology feasibility study

AbstractThe increasing volume of textile waste in landfills and incineration poses severe environmental challenges. Waste valorisation of textile waste via anaerobic digestion (AD) is preferable, as it offers economic and environmental benefits, but it is hindered by textile complexity, necessitating effective pretreatment technologies to improve biogas production. This study aims to evaluate various pretreatment technologies for biogas production from textile fibres via AD. A weighted‐scoring analysis (WSA) assessed pretreatment methods based on technical, economic, environmental and operational criteria. Hydrothermal pretreatment emerged as the most technically effective method, scoring 140 owing to its substantial methane enhancement. Economically, shredding was the most viable option, scoring 125, as a consequence of low capital and O&amp;M cost. Environmentally, hydrothermal and deep eutectic solvent (DES) pretreatments were top performers with 100 points owing to low environmental impact and positive heat reactions. In a case study conducted in the Auckland region, the potential environmental impact (PEI) obtained from hydrothermal and DES were 169 and 92 per year, respectively, resulting in minimal environmental impact. Operationally, ultrasonic and biological pretreatments scored highest owing to their ease of operation, and minimal health and safety requirements. Overall, hydrothermal pretreatment achieved the highest WSA score of 340, reflecting its balanced performance across all criteria. Hydrothermal pretreatment is the most promising technology for enhancing biogas production from textile waste. Its technical efficiency, economic feasibility and environmental benefits regarding the WSA score make it suitable for upscaling and providing a viable solution for managing textile waste in the AD plant. © 2024 The Author(s). Journal of Chemical Technology and Biotechnology published by John Wiley &amp; Sons Ltd on behalf of Society of Chemical Industry (SCI).

Research on Teaching Reform of Chemistry Experimental Course under New Engineering Background

With the proposal of new engineering education concept, new challenges are posed to the teaching reform of chemical experiment course. This study aims to explore the teaching reform path of chemistry experiment course under the new engineering background, so as to improve the students' practical ability and innovation ability. As an important part of engineering education, the teaching objectives of chemistry experiment should be consistent with the new engineering education concept, and pay attention to cultivating students' ability to solve practical problems and teamwork spirit. At present, there are some problems in chemistry experiment teaching, such as outdated curriculum system, too theoretical experiment content, lack of combination with engineering practice, and low participation of students. These problems restrict the improvement of teaching quality, and can not meet the needs of new engineering education for talent training. Therefore, the primary task of the reform is to update the experimental curriculum system and integrate more experimental projects with engineering background and cutting-edge technology, in order to improve the practicality and challenge of the experiment. In terms of reform strategies, it is suggested to implement the "student-centered" teaching mode, and encourage students to take the initiative to explore and innovate through project-driven and problem-oriented teaching methods. At the same time, strengthen the integration of experimental teaching and theoretical teaching, so that students can deepen the understanding of theoretical knowledge in the experiment, and use theoretical knowledge to solve practical problems. In addition, the information level of experimental teaching should be improved, and virtual simulation and other technologies should be used to expand the space-time dimension of experimental teaching and improve the teaching effect. The change of the role of teachers is also the key. Teachers should change from the teacher of knowledge to the guide and collaborator of students' learning. By building an open and interactive classroom environment, stimulate students' interest in learning and innovative thinking. At the same time, the establishment of a diversified evaluation system, in addition to the investigation of students' experimental skills, but also should pay attention to the evaluation of their innovative thinking, teamwork and solving practical problems. To sum up, the teaching reform of chemistry experimental curriculum under the background of new engineering should focus on the update of curriculum content, the transformation of teaching mode, the integration of teaching resources and the reform of evaluation system. Through the implementation of these strategies, it aims to build an experimental teaching system that can not only meet the needs of new engineering education, but also cultivate high-quality chemical engineering talents. Such reform is of great significance to enhance the competitiveness of chemical engineering education in China.

Nano-particles size measurement based on semantic segmentation via convolution neural network

Automatic recognition and size measurement of nanoparticles in SEM images have been an evolving field of study within the material informatics domain. These methods have been typically assessed using traditional image processing techniques. This paper presents a robust and fully automated pipeline for all researchers, particularly those in chemical engineering, based on image processing techniques and convolution neural networks. We evaluated our method on a dataset of 1,701 2D particle images, employing a three-fold split for training, validation, and testing. The proposed approach integrates semantic segmentation models (U-Net, LinkNet) with size measurement algorithms, achieving high accuracy even in the presence of noise or blurriness. Our results demonstrate the method effectiveness in precisely estimating particle sizes, with a Pearson correlation coefficient of 0.9633 and mean absolute error of 1.287e-06. The method’s robustness against poor-quality images and its fully automated procedures make it a valuable tool for researches in SEM images field.