Conventional Process Research Articles

Green process for Polyurethane: From CO2 to isocyanate

Polyurethane, a highly versatile and widely used polymer, has garnered significant interest as a candidate material for CO2 incorporation to address climate change. Nevertheless, research into alternative production methods for isocyanate, a key monomer in polyurethane production, remains insufficient, despite the heavy reliance of the conventional approach on phosgene, a highly toxic material. This study proposes a sustainable process for synthesizing methylene diphenyl diisocyanate (MDI). The proposed process involves the production of syngas from CO2, followed by syngas separation via a hydrogenation reaction, oxidative carbonylation of amine to form dicarbamate, and subsequent thermal decomposition to yield MDI. The syngas separation via the hydrogenation reaction eliminates the need for an additional separation process by simultaneously separating reaction products and preparing a reactant for the subsequent step. Moreover, the introduction of a Pd/TiO2 catalyst and a new reactant introduction strategy significantly reduces side reactions during oxidative carbonylation, thereby enhancing the overall yield. Process modeling and a life cycle assessment demonstrated substantial environmental advantages over the conventional MDI manufacturing process, highlighting the potential of this approach as a greener alternative.

Modelling Photocatalytic N2 Reduction to Ammonia: Where We Stand and Where We Are Going.

Artificial ammonia synthesis via the Haber-Bosch process is environmentally problematic due to the high energy consumption and corresponding CO emissions, produced during the reaction and before hand in hydrogen production upon methane steam reforming. Photocatalytic nitrogen fixation as a greener alternative to the conventional Haber-Bosch process enables us to perform nitrogen reduction reaction (NRR) under mild conditions, harnessing light as the energy source. Herein, we systematically review first-principles calculations used to determine the electronic/optical properties of photocatalysts, N2 adsorption and to expound possible NRR mechanisms. The most commonly studied photocatalysts for nitrogen fixation are usually modified with dopants, defects, co-catalysts and Z-scheme heterojunctions to prevent charge carrier recombination, improve charge separation efficiency and adjust a band gap to for utilizing a broader light spectrum. Most studies at the atomistic level of modeling are grounded upon density functional theory (DFT) calculations, wholly foregoing excitation effects paramount in photocatalysis. Hence, there is a dire need to consider methods beyond DFT to study the excited state properties more accurately. Furthermore, a few studies have been examined, which include higher level kinetics and macroscale simulations. Ultimately, we show there is still ample room for improvement with regard to first principles calculations and their integration in multiscale models.

Surface Chemistry Modulation of BaGd0.8La0.2Co2O6-δ As Active Air Electrode for Solid Oxide Cells.

Modulation of the surface chemistry of air electrodes makes it possible to significantly improve the electrocatalytic performance of solid oxide cells (SOCs). Here, the surface chemistry of BaGd0.8La0.2Co2O6-δ (BGLC) double perovskite is modulated by treatment in an acidic citric acid solution. The treatment leads to corrosion on the surface of BGLC particles, and the effect is dependent on the acidity of the solution. As the acidity of solution is low, Ba cations are selectively dissolved out of the BGLC surface, while as the acidity increases, the corrosion becomes more homogeneous. The Ba surface deficiency remarkably increases the concentration of surface oxygen vacancies and electrocatalytic activity of BGLC. To avoid the loss of Ba-deficient surface during the conventional high temperature sintering process, a sintering-free fabrication route is utilized to directly assemble the Ba-deficient BGLC powder into an air electrode. A single cell with the surface Ba-deficient BGLC electrode shows a peak power density of 1.04 W cm-2 at 750 °C and an electrolysis current density of 1.48 A cm-2 at 1.3 V, much greater than 0.64 W cm-2 and 1.02 A cm-2 of the cell with the pristine BGLC, respectively. This work provides a simple and effective surface chemistry modulation strategy for the development of an efficient air electrode for SOCs.

Engineered lignocellulosic based biochar to remove endocrine-disrupting chemicals: Assessment of binding mechanism

The safety and health of aquatic organisms and humans are threatened by the increasing presence of pollutants in the environment. Endocrine disrupting chemicals are common pollutants which affect the function of endocrine and causes adverse effects on human health. These chemicals can disrupt metabolic processes by interacting with hormone receptors upon consumptions by humans or aquatic species. Several studies have reported the presence of endocrine disrupting chemicals in waterbodies, food, air and soil. These chemicals are associated with increasing occurrence of obesity, metabolic disorders, reproductive abnormalities, autism, cancer, epigenetic variation and cardiovascular risk. Conventional treatment processes are expensive, not environment friendly and unable to achieve complete removal of these harmful chemicals. In recent years, biochar from different sources has gained a considerable interest due to their adsorption efficiency with porous structure and large surface areas. biochar derived from lignocellulosic biomass are widely used as sustainable catalysts in soil remediation, carbon sequestration, removal of organic and inorganic pollutants and wastewater treatment. This review conceptualizes the production techniques of biochar from lignocellulosic biomass and explores the functionalization and interaction of biochar with endocrine-disrupting chemicals. This review also identifies the further needs of research. Overall, the environmental and health risks of endocrine-disrupting chemicals can be dealt with by biochar produced from lignocellulosic biomass as a sustainable and prominent approach.

Deep learning restores speech intelligibility in multi-talker interference for cochlear implant users

Cochlear implants (CIs) do not offer the same level of effectiveness in noisy environments as in quiet settings. Current single-microphone noise reduction algorithms in hearing aids and CIs only remove predictable, stationary noise, and are ineffective against realistic, non-stationary noise such as multi-talker interference. Recent developments in deep neural network (DNN) algorithms have achieved noteworthy performance in speech enhancement and separation, especially in removing speech noise. However, more work is needed to investigate the potential of DNN algorithms in removing speech noise when tested with listeners fitted with CIs. Here, we implemented two DNN algorithms that are well suited for applications in speech audio processing: (1) recurrent neural network (RNN) and (2) SepFormer. The algorithms were trained with a customized dataset (∼\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim$$\\end{document} 30 h), and then tested with thirteen CI listeners. Both RNN and SepFormer algorithms significantly improved CI listener’s speech intelligibility in noise without compromising the perceived quality of speech overall. These algorithms not only increased the intelligibility in stationary non-speech noise, but also introduced a substantial improvement in non-stationary noise, where conventional signal processing strategies fall short with little benefits. These results show the promise of using DNN algorithms as a solution for listening challenges in multi-talker noise interference.

Stakeholders’ justifications in innovation: the case of cell-based meat

Cell-based meat (CBM) is a novel technology that employs the cultivation of animal cells in bioreactors to produce edible meat, representing a breakthrough innovation in the meat production chain. However, innovations are embedded in uncertainty and ambiguity; thus, several stakeholders shape their development and legitimacy. Stakeholders attempt to set their priorities by employing and disseminating justifications when facing novelty situations. This study builds on stakeholder and justification theories to explore the justifications employed by stakeholders and their impacts on innovations such as CBM technology. To this end, interviews with representatives of five stakeholder groups were conducted - startups, NGOs, investors, researchers, and conventional animal-based meat processing firms - and triangulated with secondary data from websites and news. Public Justification Analysis was applied to analyze the data. The results indicate that the analyzed stakeholders converge toward CBM legitimacy and development by sharing similar purposes and values. On the other hand, disputes and justifications contrary to CBM legitimacy are mainly related to traditional values, which can raise obstacles to product development. Hence, by analyzing stakeholders’ justifications, we can understand their behaviors and demonstrate how they shape innovation and a nascent high-tech industry.

Deep learning promoted target volumes delineation of total marrow and total lymphoid irradiation for accelerated radiotherapy: A multi-institutional study

Background and purposeOne of the current roadblocks to the widespread use of Total Marrow Irradiation (TMI) and Total Marrow and Lymphoid Irradiation (TMLI) is the challenging difficulties in tumor target contouring workflow. This study aims to develop a hybrid neural network model that promotes accurate, automatic, and rapid segmentation of multi-class clinical target volumes. Materials and methodsPatients who underwent TMI and TMLI from January 2018 to May 2022 were included. Two independent oncologists manually contoured eight target volumes for patients on CT images. A novel Dual-Encoder Alignment Network (DEA-Net) was developed and trained using 46 patients from one internal institution and independently evaluated on a total of 39 internal and external patients. Performance was evaluated on accuracy metrics and delineation time. ResultsThe DEA-Net achieved a mean dice similarity coefficient of 90.1 % ± 1.8 % for internal testing dataset (23 patients) and 91.1 % ± 2.5 % for external testing dataset (16 patients). The 95 % Hausdorff distance and average symmetric surface distance were 2.04 ± 0.62 mm and 0.57 ± 0.11 mm for internal testing dataset, and 2.17 ± 0.68 mm, and 0.57 ± 0.20 mm for external testing dataset, respectively, outperforming most of existing state-of-the-art methods. In addition, the automatic segmentation workflow reduced delineation time by 98 % compared to the conventional manual contouring process (mean 173 ± 29 s vs. 12168 ± 1690 s; P < 0.001). Ablation study validate the effectiveness of hybrid structures. ConclusionThe proposed deep learning framework achieved comparable or superior target volume delineation accuracy, significantly accelerating the radiotherapy planning process.

Organic radicals driving polycyclic aromatic hydrocarbon polymerization with peracetic acid activation in soil

The use of peracetic acid (PAA) in advanced oxidation processes has gained significant attention recently, but the knowledge of activating PAA to degrade polycyclic aromatic hydrocarbons (PAHs) is limited due to the variety and selectivity of reactive substances in PAA oxidation system. This paper presented the first systemically study on the degradation of PAHs by PAA activation in soil. It was found that heat-activated peracetic acid (heat/PAA) was capable of degrading phenanthrene (PHE) efficiently with degradation efficiency > 90 % within 30 min. Experimental results demonstrated that a series of reactive oxygen species (ROS) including organic radicals (RO•), hydroxyl radicals (HO•) and singlet oxygen (1O2) were generated, while acetylperoxyl (CH3C(O)OO•) and acetyloxyl (CH3C(O)O•) radicals were primarily responsible for PHE degradation in soil. Further analysis shows that polymerization products such as diphenic acid, 2′-formyl-2-biphenylcarboxylic acid and other macromolecules were dominant products of PHE degradation, suggesting polymerization driving PHE degradation instead of the conventional mineralization process. Toxicity analysis shows that most of the polymerization products had less toxicity than that of PHE. These results indicate that PAA activation was a highly effective remediation method for PAHs contaminated soil, which also provided a novel mechanism for pollutant degradation with the PAA activation process for environmental remediation.

Metal-Organic Framework-Supported Mono Bipyridyl-Iron Hydroxyl Catalyst for Selective Benzene Hydroxylation into Phenol.

Direct hydroxylation of benzene to phenol is more appealing in the industry for the economic and environmentally friendly phenol synthesis than the conventional cumene process. We have developed a UiO-metal-organic framework (MOF)-supported mono bipyridyl-Iron(II) hydroxyl catalyst [bpy-UiO-Fe(OH)2] for the selective benzene hydroxylation into phenol using H2O2 as the oxidant. The heterogeneous bpy-UiO-Fe(OH)2 catalyst showed high activity and remarkable phenol selectivity of 99%, giving the phenol mass-specific activity up to 1261 mmolPhOHgFe-1 h-1 at 60 °C. Bpy-UiO-Fe(OH)2 is significantly more active and selective than its homogeneous counterpart, bipyridine-Fe(OH)2. This enhanced catalytic activity of bpy-UiO-Fe(OH)2 over its homogeneous control is attributed to the active site isolation of the bpy-Fe(OH)2 moiety by the solid MOF that prevents intermolecular decomposition. Moreover, the exceptional selectivity of bpy-UiO-Fe(OH)2 in benzene to phenol conversion is originated via shape-selective catalysis, where the confined reaction space within the porous UiO-MOF prevents the formation of larger overoxidized products such as hydroquinone or benzoquinone, leading to the formation of only smaller-sized phenol after monohydroxylation of benzene. Spectroscopic and controlled experiments and theoretical calculations elucidated the reaction pathway, in which the in situ generated •OH radical mediated by bpy-UiO-FeII(OH)2 is the key species for benzene hydroxylation. This work underscores the significance of MOF-supported earth-abundant metal catalysts for sustainable production of fine chemicals.

Simple route to produce excellent mechanical properties of 9Mn steel with heterostructure induced by pre-existing austenite with sub-rapid solidification

Medium-Mn steels have garnered significant interest due to their unique combination of high strength and exceptional ductility. However, a high Mn content inevitably causes the macro-segregation of Mn, which can significantly compromise the mechanical properties. This study effectively prevented the formation of Mn macro-segregation by employing the sub-rapid solidification. It resulted in the distribution of Mn micro-segregation bands within dendritic gaps in Fe-9Mn-2Si-0.5Al-0.1C steel. Mn micro-segregation bands results in a high proportion of pre-existing austenite (γpre) with small size and suitable stability in the as-cast strip, forming a lamellar structure with alternating distribution of lath martensite and austenite on the micrometer scale. The as-cast strip necessitates only basic simple hot rolling control without the need for any tempering treatment to achieve excellent mechanical properties comparable to conventional lengthy processes (ultimate tensile strength (UTS) of 1762 MPa, total elongation (TE) of 20 %, and product of strength and elongation (PSE) up to 35 GPa%). Utilizing this structure as a foundation, a simple austenite reverse tempering (ART) treatment can lead to additional enhancements in comprehensive mechanical characteristics with the UTS of 1529 MPa, TE of 25 % and PSE of 38 GPa%.

An Extensive Analysis of Combined Processes for Landfill Leachate Treatment

Sanitary landfilling is the predominant process for solid urban waste disposal, but it generates leachate that poses environmental, economic, and social concerns. Landfill leachate (LL) contains complex and refractory pollutants and toxic compounds that can vary depending on landfill maturity, age, and biochemical reactions, making its treatment challenging. Due to its unique characteristics and occurrence in remote locations, LL requires separate treatment from wastewater. Various conventional treatment processes involving biological, chemical, and physical processes have been used for LL treatment, but a single treatment process is insufficient to meet environmental standards. This review demonstrates that combined treatment processes are more effective and efficient for LL treatment compared to single processes. Among the various combinations, chemical–chemical and chemical–biological treatments are the most commonly used. Specifically, the integration of Fenton with adsorption and a membrane bioreactor (MBR) with nanofiltration (NF) processes shows promising results. The combined processes of MBR with NF, Fenton with adsorption, and PF with biological treatment show maximum removal efficiencies for COD, reaching 99 ± 1%, 99%, 98%, and 97%, respectively. Additionally, the combined Fenton with adsorption process and EC with SPF process enhance biodegradability as indicated by increased BOD5/COD ratios, from 0.084 to 0.82 and 0.35 to 0.75, respectively. The findings emphasize the importance of developing and implementing enhanced combined treatment processes for LL, with the aim of achieving efficient and comprehensive pollutant mineralization. Such processes have the potential to address the environmental concerns associated with LL and contribute to sustainable waste management practices.

Extendable machine tool wear monitoring process using image segmentation based deep learning model and automatic detection of depth of cut line

Automating the monitoring of machine tools poses a significant challenge, with previous studies relying on machine vision to address this issue, primarily focusing on measuring specific tool wear using custom algorithms. In this study, we introduce an automated tool wear monitoring process capable of assessing wear across various tools. Utilizing deep neural network models for image classification and segmentation, our proposed process effectively masks areas where tool wear occurs. The image classification model selects the best-performing backbone model based on the highest F1 score. To account for varying depths-of-cut lines among different tools in the masked area, we introduce an algorithm that utilizes the Hough transform to determine the horizontal angle with the cut line. By systematically measuring the maximum flank wear of diverse tools, including indexable, ball, and solid endmills, using this method, we can replace conventional manual processes. This approach can be extended and automated for various machine tools, offering substantial potential for enhancing manufacturing tool monitoring processes.

Wire-cut electrical discharge machining of duplex stainless steel: a study

ABSTRACT In this study, the duplex stainless steel is machined using a wire-cut electrical discharge machining process to overcome the issues of poor finishing, lower dimensional accuracy, and rapid wear of cutting tools in conventional machining processes. The higher rate of material removal and superior finish on the machine surface is dependent on kerf loss, which is expressed as the mean kerf width assessed at certain locations. Nevertheless, this approach is typically not effective in explicating the intricacies of true kerf loss due to the random selection of distinctive points. This work proposes an area-based measuring approach to quantify the kerf loss by taking into account any imperfections on the kerf wall. At the pulse on time of 20 μs and current of 3 A, the machining appears not stable. The pulse on time, current, and voltage of 12 µs, 1 A, and 80 V proved suitable for obtaining excellent dimensional precision.

Preparation of a moisture-permeable glazed tile to prevent indoor floor condensation

Condensation would occur on smooth tiled floors during the period from winter to spring after rainy or continuous wet days in the hot-humid climate. It is an effective passive approach to use moisture-permeable tiles for mitigating surface condensation on indoor floors. This study proposes a moisture-permeable glazed tile prepared by the conventional tile-making process. The base tile of the proposed glazed tile is primarily composed of kaolin, quartz and feldspar, with addition of soluble starch as a pore-forming agent before firing. The glaze layer is formed by spraying a slurry which is a mixture of a low-temperature glaze and the pore-forming agent (soluble starch). The present study investigated the impact of the pore-forming agent ratio on tile porosity, wet capacity, breaking strength, moisture permeability, and abrasion resistance. It was found that the increase of the pore-forming agent ratio in the base tile can enhance the porosity and moist capacity of the base tile, and weaken the breaking strength. Moreover, the increase of the pore-forming agent in the glaze can improve moisture permeability of the glaze and reduce abrasion resistance. Through iterative experiments, the ratio of the pore-forming agent was optimized as 7.5 wt% for the base tile and 10 wt% for the glaze layer under consideration of the balance between mechanical properties and moisture absorption/permeability. Moisture capacity, water absorption and porosity of the prepared glazed tile are 0.469 g/cm³, 25.7 % and 37.7 %, respectively. The compressive breaking strength is more than 600 N (the standard for floor tiles), and the abrasion resistance of the glazed surface reaches Level 2 (meeting the standard for indoor use). Furthermore, the observation in a high-humidity environment reveals that the moisture-permeable glazed tile is capable of controlling condensation on its surface throughout a 24 h-period.

Experimental assessment and optimization of the performance of a biodiesel engine using response surface methodology

BackgroundBiodiesel is a renewable and ecofriendly fuel for internal combustion engines. However, fuel standards need to be adapted for efficiency and commercial use. This paper deals with a novel process of its production using a purification step that counters the high costs of production and experimental analysis using multiresponse optimization.MethodsSoybean oil was chosen as a biodiesel of 5%, 10%, and 15% blend with common diesel fuel and is experimentally tested in a variable compression ratio compression ignition engine. The biodiesel is blended with common diesel fuel to run the engine without any modification in its setup, which also solves most of the operational problems. The functional relationship between the input parameters and the performance characteristics of the engine is evaluated by statistical response surface methodology using the Box–Behnken design model, which generates a design of experiment resulting in an optimum experimental run that reduces the overall cost of the experimental investigation. Uncertainty analysis is done to minimize the gap between the results considering the errors of each piece of equipment. Validation of the results is also carried out.ResultsThe analysis of variance is used to measure the acceptability of the model and the competency of the model to predict output performance. The optimum value of input parameters which are obtained are 4.5 kg for the load, the compression ratio of 18, and B05 for the fuel blend, which results in maximum performance of brake power of 3 kW, minimum fuel consumption and emissions of CO and NOx, which are 0.39 kg/kWh, 0.01%, and 50 ppm.ConclusionsCost analysis reveals that biodiesel produced from the novel process of transesterification is reasonable as compared with the conventional process. It is also environmentally more sustainable, which cannot be ignored. This technique can be used in future research for cost-effective production fields such as combustion parameters and biofuels produced from waste, which need to be explored.

Tissue Processing using Microwave Oven: A Boon for Histology Slide Preparation

Background: Tissue processing is an important step in histology laboratories. Routine tissue processing is time-consuming. Due to the same time factor, microwave tissue processing is now increasingly being used in many histology and pathology laboratories. Objective: To compare the time taken to process the tissues using the routine conventional and microwave methods. Methods: The study was conducted in the Histology laboratory. 100 slides (50 conventional method + 50 microwave method) were processed using both methods, sectioned, stained, and analyzed by an independent observer who was unaware of the processing method. Results: The study showed a significant decrease in processing time by the microwave method (p-value &lt; .0001). Conclusion: The present study concluded that the time taken for dehydrating, clearing, and embedding the tissues in paraffin wax was found to be considerably lower than that taken for the conventional method of tissue processing. Chloroform was used here as a clearing agent, and it had desirable effects. KEYWORDS: Microwave tissue processing, Conventional tissue processing.

4D printing of biobased shape memory sandwich structures

Lightweight sandwich structures already have many applications in the aerospace, automotive, and construction sectors due to their high strength-to-weight ratio, enhanced stiffness and rigidity, and high energy and shock absorption capabilities. Additive manufacturing (AM) can provide great freedom and flexibility in the fabrication of complex sandwich structures that cannot be made by other conventional processes. The application of smart materials, like shape memory polymers (SMP), can offer the capability of dynamic transformation so that the deformed structure can recover its original shape using external stimuli, such as temperature. This study exploits the potential of this technology in the design and manufacturing of biobased smart sandwich structures with energy absorption capability and shape recovery performance. For that, a combination of recycled poly(lactic acid) (PLA)/poly(butylene succinate) (PBS) biopolymers are developed as printing filaments with shape memory properties. The designed sandwich samples are fabricated by Fused Filament Fabrication (FFF) technology. The mechanical and functional properties of the printed samples are also evaluated. The results show PLA/PBS filaments have good printability and the fabricated parts have acceptable dimensional accuracy, mechanical strength, and functional performance.

Use of Zero-Valent Iron Nanoparticles (nZVIs) from Environmentally Friendly Synthesis for the Removal of Dyes from Water—A Review

This review article addresses the increasing environmental concerns posed by synthetic dyes in water, exploring innovative approaches for their removal with a focus on zero-valent iron nanoparticles (nZVIs) synthesized through environmentally friendly methods. The article begins by highlighting the persistent nature of synthetic dyes and the limitations of conventional degradation processes. The role of nanoparticles in environmental applications is then discussed, covering diverse methods for metallic nanoparticle production aligned with green chemistry principles. Various methods, including the incorporation of secondary metals, surface coating, emulsification, fixed support, encapsulation, and electrostatic stabilization, are detailed in relation to the stabilization of nZVIs. A novel aspect is introduced in the use of plant extract or biomimetic approaches for chemical reduction during nZVI synthesis. The review investigates the specific challenges posed by dye pollution in wastewater from industrial sources, particularly in the context of garment coloring. Current approaches for dye removal in aqueous environments are discussed, with an emphasis on the effectiveness of green-synthesized nZVIs. The article concludes by offering insights into future perspectives and challenges in the field. The intricate landscape of environmentally friendly nZVI synthesis has been presented, showcasing its potential as a sustainable solution for addressing dye pollution in water.

Critical Review on Two-Stage Anaerobic Digestion with H2 and CH4 Production from Various Wastes

Anaerobic digestion (AD) is a promising method for resource recovery from various wastes. Compared to the conventional single-stage AD process, a two-stage AD process with separate H2 and CH4 production provides higher energy recovery efficiency and enhanced operation stability. The stage separation makes it possible to apply optimal conditions for different functional microorganisms in their respective stages. This review elaborates the mechanisms of the two-stage AD process and evaluates recent research trends on this topic. A comprehensive comparison between single- and two-stage AD processes is made from the perspective of biogas production, organics degradation, energy recovery, and operation stability. The main influence factors on the two-stage AD process are discussed, including substrates, inoculum, and operation parameters, such as pH, temperature, etc. Upgrading technologies for the two-stage AD process are assessed. The microbial communities in the two-stage AD process for treating different substrates and the influence factors on microbial systems are also summarized. Furthermore, future research opportunities for enhancing the application of this technology are highlighted.

Wet mechanochemical surface modification of calcite employing an integration of conventional design and analytical hierarchy process

The demand for ultra-fine mineral powders from various industries requires the applications of wet grinding and surface modification. In this study, wet mechanochemical surface modification of micronized calcite (d50 = 4.92 μm) with stearic acid [CH3(CH2)16COOH] was carried out in a planetary ball mill. The seven parameters were specified: ball filling ratio (%), ball size distribution ratio (10 and 15 mm, %), micronized calcite filling ratio (%), pulp solid ratio (wt%), mill speed (rpm), stearic acid dosage (% of micronized calcite) and modification time (min). Then, these parameters were optimized using a conventional experimental design when maximizing the active ratio (%) as a significant value for coated calcite. Besides, the analytical hierarchy process was applied to observe the importance of each parameter. Stearic acid dosage, mill speed, and micronized calcite filling ratio are the top three parameters affecting the active ratio. Next, size distribution, XRF, XRD, SEM, FTIR, TGA-DTA, contact angle, and whiteness measurements were performed on the micronized calcite (MC) and coated micronized calcite (CMC) samples. The final CMC product had a 99.90% active ratio, 2.49 μm mean (d50) particle size, and 101.66o contact angle. Finally, CMC obtained by the surface modification can be used as a filler mineral.