Influence Of Process Variables Research Articles

Brownian dynamics simulation of the structural evolution in monodisperse hard-sphere suspensions during drying and sedimentation processes.

The drying behavior of monodisperse colloidal films, with a focus on the influence of process variables on film microstructures, is explored via Brownian dynamics (BD) simulations. In our model, hard-sphere colloidal particles are dispersed in a Newtonian liquid with an initial particle volume fraction of 0.1. The effects of the drying rate and sedimentation on the evolving microstructures are systematically investigated using two dimensionless numbers: the Péclet number (Pe), which represents the competition between evaporation and diffusion, and the sedimentation number (Ns), which reflects the relative influence of sedimentation on evaporation. First, we analyze the local particle volume fraction and film structure at various Pe and Ns. As Pe increases, particle accumulation occurs near the liquid-gas interface, whereas a high Ns promotes dense packing near the substrate owing to sedimentation. The BD simulation results, viz. the local volume fraction profiles and drying regime maps, are in good agreement with those of the continuum model proposed by Wang and Brady. Structural analysis of the dried films reveals that at a low Pe (Pe = 0.1), a face-centered cubic (FCC) structure dominates, primarily independent of the sedimentation effects. In contrast, a high Pe leads to hexagonal close-packed or amorphous structure formation. Notably, at intermediate drying rates (Pe = 10), an increase in Ns promotes additional FCC ordering in the final film structure. Our study provides new insights into the hitherto underexplored role of sedimentation in the structural evolution of drying colloidal films, revealing the mechanisms of drying-induced assembly in colloidal systems.

Process optimization and finite element modeling in magnetorheological abrasive finishing of SS-316L with surface characterization

This study explores the potential of Magnetorheological Abrasive Finishing (MRAF) for ultra-precision finishing of SS-316L using a high-performance Nd-Fe-B (N-35 grade) magnetic tool. Key parameters, including the relationship between normal force (Fn) and working gap, magnetic flux distribution, and finishing spot area, were analyzed using ANSYS 2020 R1® Magnetostatic module simulations. Material removal rate (MRR) and surface roughness (Ra) were optimized through response surface methodology (RSM) using a central composite design (CCD) with two primary input factors: SiC abrasive volume fraction (5%, 10%, 15%) and abrasive mesh size (800, 1000, 1200). Statistical analysis via ANOVA revealed a significant influence of process variables, achieving a remarkable surface finish with a minimum Ra of 52 nm in just 60 min, utilizing an MR fluid with 10% SiC abrasives. Microscopic and spectroscopic evaluations confirmed enhanced surface morphology and microstructural refinement. Moreover, bioactivity studies in simulated body fluid highlighted improved hydroxyapatite layer formation on the finished SS-316L samples, underscoring the process’s potential for biomedical applications.

Lignocellulose Treatment Using a Flow-Through Variant of OrganoCat Process.

This study adapts the biphasic OrganoCat system into a flow-through (FT) reactor, using a heated tubular setup where a mixture of oxalic acid and 2-methyltetrahydrofuran (2-MTHF) is pumped through beech wood biomass. This method minimizes solvent-biomass contact time, facilitating rapid product removal and reducing the risk of secondary reactions. A comparative analysis with traditional batch processes reveals that the FT system, especially under severe conditions, significantly enhances extraction efficiency, yielding higher amounts of lignin and sugars with reduced solid residue. Notably, the FT system shows partial hydrolysis of the cellulose, which increases with temperature while not producing significant amounts of furfural or 5-HMF, indicating more efficient depolymerization of polysaccharides without substantial sugar degradation. A statistical design of experiments (DOE) using a Box-Behnken design elucidates the influence of process variables (time, solvent flow rate, temperature) on the yield. Key findings highlight reactor temperature as the dominant factor affecting yields, with process time showing a significant but less pronounced impact. This study demonstrates the potential of the FT OrganoCat system for efficient lignocellulosic biomass fractionation and represents an advancement towards continuous lignocellulose processing, contributing to our knowledge of process optimization for improved biorefinery applications.

Investigation of wear characteristics of granite dust & coconut shell ash reinforced aluminum metal matrix composite using design of experiment

Recently, one of the most important issue faced by all nations is a waste management. Therefore, it is essential to search for creative solutions to waste reduction, reuse, and recycling. This can be accomplished by adding waste materials to composite materials as a reinforcements. Reusing waste materials enhances the characteristics of existing materials, while also helping the environment by solving the disposal problem. The main aim of this paper is to study the wear characteristics of hybrid aluminum metal matrix composite reinforced with coconut shell ash (CSA) and granite dust. The hybrid composite samples were manufactured using stir casting by reinforcing CSA (ranging from 0 wt% to 12 wt%) and granite dust (maintained at 2 wt%) in Al6061 alloy. SEM and EDAX analysis were performed to investigate the microstructure and elemental composition. The wear characteristics of the hybrid composites were determined via pin-on-disc tests. Finally, the Taguchi design of experiment was performed on the specimen having the best wear characteristics by selecting the L27 orthogonal array and identified the influence of process variables on the wear rate. The results of the pin-on-disc experiment showed that the wear rate decreased with increasing CSA%. The hybrid composite composed of 02 wt% granite dust, & 12 wt% CSA indicates a lower wear rate (56.25%) as compared to the matrix alloy. The Taguchi analysis prooved that the sliding distance has a greater impact on the wear rate than the other parameters. This material can be a superior substitute where high wear resistance is required.

Analysis on fiber laser marking characteristics of elliptical shaped geometry marked on SS 304

ABSTRACT Laser marking is well known thermal-based permanent marking employed by organisation as a part of their product identity. It laid emphasis on minimum material removal from workpiece surface with better surface quality characteristics. There are generally two modes of laser marking one with material removal and other is surface modification. The paper presents the generation of elliptical shaped laser marked surface and also to study the influence of process variables in the generation of uniform laser marked surface on stainless steel 304. Experimental results revealed that process variables such as laser irradiation, scan rate, pulse rate, and defocused distance play an important role in achieving better marking characteristics.

Dual temporal attention mechanism-based convolutional LSTM model for industrial dynamic soft sensor

Abstract Deep learning is an appropriate methodology for modeling complex industrial data in the field of soft sensors, owing to its powerful feature representation capability. Given the nonlinear and dynamic nature of the process industry, the key challenge for soft sensor technology is to effectively mine dynamic information from long sequences and accurately extract features of relevance to quality. A dual temporal attention mechanism-based convolutional long short-term memory network (DTA-ConvLSTM) under an encoder-decoder framework is proposed as a soft sensor model to acquire quality-relevant dynamic features from serial data. Considering different influences of process variables for prediction at multiple time steps and various locations, ConvLSTM and temporal self-attention mechanism are utilized as the encoder to adaptively fuse spatiotemporal features and capture long-term dynamic properties of process in order to capture the trends of industrial variables. Furthermore, a quality-driven temporal attention mechanism is employed throughout the decoding process to dynamically select relevant features to more accurately track quality changes. The encoder-decoder model meticulously analyses the interactions between process and quality variables by incorporating dual-sequence dynamic information to improve the prediction performance. The validity and superiority of the DTA-ConvLSTM model was validated on two industrial case studies of the debutanizer column and sulfur recovery unit. Compared to the traditional LSTM model, the proposed model demonstrated a substantial improvement with the accuracy R2 up to 97.3% and 94.9% and the root mean square error reducing to 0.122 and 0.022.

Study of a Copper Oxide Leaching in Alkaline Monosodium Glutamate Solution

Oxide copper minerals are commonly extracted via acidic leaching, using acids such as H2SO4, HCl, or HNO3. These strong acids are the most widely used because of their high dissolution kinetics. However, their main concern is the high acid consumption because copper oxide deposits contain large amounts of acid-consuming gangue. This paper proposes using an alternative aqueous alkaline monosodium glutamate (MSG) system to leach copper oxide minerals. Tenorite (CuO) was used as the copper oxide mineral under study. The influence of process variables (such as temperature and glutamate concentration) and kinetics of this system on copper leaching from tenorite were studied. The results showed that temperature has a significant effect on copper dissolution rates. Increased temperature from 15 °C to 60 °C enhanced the copper extraction from 9.1% to 97.7% after 2 h. Leaching kinetics were analyzed using the shrinking core model (SCM) under various conditions, indicating that the leaching rate presented a mixed control. This method, however, fails to describe leaching for broad particle sizes due to its requirement for single-sized solid grains. This study demonstrated that a large particle size distribution in tenorite supported a successful extension of the SCM for leaching it from mixed glutamate solutions. The activation energy for the 15–60 °C temperature range was calculated to be 102.6 kJ/mol for the chemical control.

Advanced Machine Learning Techniques for Corrosion Rate Estimation and Prediction in Industrial Cooling Water Pipelines.

This paper presents the results of a study on data preprocessing and modeling for predicting corrosion in water pipelines of a steel industrial plant. The use case is a cooling circuit consisting of both direct and indirect cooling. In the direct cooling circuit, water comes into direct contact with the product, whereas in the indirect one, it does not. In this study, advanced machine learning techniques, such as extreme gradient boosting and deep neural networks, have been employed for two distinct applications. Firstly, a virtual sensor was created to estimate the corrosion rate based on influencing process variables, such as pH and temperature. Secondly, a predictive tool was designed to foresee the future evolution of the corrosion rate, considering past values of both influencing variables and the corrosion rate. The results show that the most suitable algorithm for the virtual sensor approach is the dense neural network, with MAPE values of (25 ± 4)% and (11 ± 4)% for the direct and indirect circuits, respectively. In contrast, different results are obtained for the two circuits when following the predictive tool approach. For the primary circuit, the convolutional neural network yields the best results, with MAPE = 4% on the testing set, whereas for the secondary circuit, the LSTM recurrent network shows the highest prediction accuracy, with MAPE = 9%. In general, models employing temporal windows have emerged as more suitable for corrosion prediction, with model performance significantly improving with a larger dataset.

Testing the disintegration and texture-related palatability predictions for orodispersible tablets using an instrumental tool coupled with multivariate analysis: Focus on process variables and analysis settings

Orodispersible tablets (ODTs) represent a growing category of dosage forms intended to increase the treatment acceptability for special groups of patients. ODTs are designed to rapidly disintegrate in the oral cavity and to be administered without water. In addition, ODTs are easy to manufacture using standard excipients and pharmaceutical equipment.This study adds to previously published research that developed an instrumental tool to predict oral disintegration and texture-related palatability of ODTs with different formulations. The current study aimed to challenge the predictive capacity of the models under variable process conditions. The studied process parameters with potential impact on the pharmaceutical properties, texture profiles, and palatability were the compression pressure, punch shape and diameter. Subsequently, for all the placebo and drug-loaded ODTs, the in vivo disintegration time and texture-related palatability were determined with healthy volunteers.Previously developed regression models were applied to predict the formulation's disintegration time and texture-related palatability characteristics of ODTs obtained under different experimental conditions. The influence of process variables on the predictive performance of the models was estimated by calculating the residuals as the difference between the predicted and observed values for the investigated response. Increasing the speed of the analyser`s probe from 0.01 mm/s to 0.02 mm/s led to an improved differentiation of the texture profiles. The in vivo disintegration time and texture-related palatability scores were only influenced by the mechanical resistance and the tablet shape. Lower score was observed for the larger diameter tablets (10 mm). Overall, the prediction of the disintegration time at 0.02 mm/s was more accurate, except for stronger tablets. The best prediction of texture-related palatability was achieved for the 10 mm tablets, tested at 0.01 mm/s speed.The same model achieved good predictions of the oral disintegration time for all API-loaded formulations, which confirmed the ability of the texture analysis to capture process-related variability. Drug loading decreased the predictive capacity of the texture-related palatability because of the taste effect.

Optimizing the SPIF parameters for enhancing microhardness and surface quality in Inconel 625 superalloy components

Inconel 625, a high-strength superalloy with excellent corrosion resistance, discovers wide applications in critical sectors such as aerospace, nuclear, marine, and petrochemical. Single-Point Incremental Forming (SPIF) has appeared as an adequate fabricating process for shaping superalloy components. This work used a systematic approach, implementing Response Surface Methodology (RSM) to explore the influence of SPIF process variables, including tool nose diameter, step size, tool spindle speed, and wall angle, on microhardness (MH) and surface roughness (SR). The experimental results have been analyzed to determine the influencing process variables and their optimal settings, which simultaneously enhance MH and reduce SR of Inconel 625 superalloy truncated cones using analysis of variance and the desirability function analysis approach. The most significant parameters that influence MH and SR were TND and SS, respectively. The development of quadratic models for MH and SR has been completed successfully. The maximum MH of 486.8 HV and minimum SR of 0.432 µm have been obtained at the optimal parametric setting of TND = 15 mm, SS = 0.4 mm, TSS = 900 rpm, and WA = 57.5° with the desirability values for MH, SR and combined are 0.832, 0.933, 0.881 respectively. The F-value of the MH model = 109.18 and SR Model = 78.24 implies that both models are significant and both models have excellent prediction strength. The percentage error between predicted and actual values of MH and SR in confirmation run results is less than 5 %.

Multi-objective optimization of wire electrical discharge machining process using multi-attribute decision making techniques and regression analysis

Wire electrical discharge machining (WEDM) is one of the most important non-traditional machining methods that is widely used in various industries. The present research work is concerned with the influences of process variables on quality of machined specimen obtained from WEDM process. The process parameters to manufacture mold structure included wire feed speed, wire tension and generator power, and in the current research, the effects of these variables on the aim factors, namely dimensional accuracy, hardness and roughness of product surface have been investigated, simultaneously. In order to obtain the optimal experiment, the multi-objective optimization with discrete solution area has been employed. Method based on the removal effects of criteria (MEREC) and weighted aggregates sum product assessment (WASPAS) techniques have been used with the aim of weighting the objective functions and discovering the best practical experiment. In the following, the regression analysis has been employed to study the effects of variables on response factors. A good correlation between the results gained from two analysis methods was observed. Based on MEREC-WASPAS hybrid technique, the weights of roughness, hardness and dimensional accuracy of machined part were calculated to about 89%, 9% and 2%, respectively. In the selected optimal experiment, the amount of wire feed speed, wire tension and generator power variables were considered to, in turn, 2 cm/s, 2.5 kg, and 10%.

Experimental investigation during nano powder added µ-ED milling on nickel-titanium shape memory alloy

Nickel-titanium (NiTi) shape memory alloy (SMA) is one of the smart materials which has a vast application in the aerospace and biomedical industry due to its shape memory effect (SME), strong corrosion and wear resistance. Thus, the present study investigates the influence of process variables like gap voltage (V), powder concentration (PC) and pulse on time (ton) towards the material removal rate (MRR), surface roughness (SR) and micro-hardness (MH) amid graphene nano powder added micro-electrical discharge milling (µ-ED milling) of NiTi SMA. ANOVA analysis has been carried out to determine the % distribution of individual machining parameters towards all responses. The addition of graphene nano particles to the dielectric oil considerably enhanced the MRR, MH, and decreased the SR of the milled micro-channel. Taguchi’s grey relational analysis (GRA) and grey-desirability approach have been applied for multi-response optimisation to find the maximum MRR, MH, and minimum SR. It is discovered that the grey-desirability method improves all the responses. The Field emission scanning electron microscopy (FESEM) micro-graphs confirm defect-free mirror-like surface finish at the optimal grey-desirability setting. The X-ray diffraction (XRD) analysis discovered the formation of oxides and hardened phases in the machined surface. The application of graphene enhanced the recast layer thickness and reduced the indentation depth at subsurface region. The nano indentation test validates the rise in nano hardness and elastic modulus at the grey desirability optimal setting compared to the initial machining condition.

Parametric Optimization of Selective Laser Melted 13Ni400 Maraging Steel by Taguchi Method

This study’s novel 13Ni400 maraging steel parts are additively manufactured through a selective laser melting process. The Taguchi approach is adopted to evaluate the combined influence of process variables (energy density), viz., laser power, layer thickness, hatch spacing, and scan speed, on responses like relative density, microhardness, surface roughness, and tensile strength. The powder and material characterization studies are conducted in terms of an optical microscope, scanning electron microscope (SEM), electron backscatter diffraction (EBSD), X-ray diffraction (XRD), and fractography analysis to explore the pre- and post-fabrication scenarios of the build parts. The consequences of energy density and process variables are studied through meticulous parametric studies. Finally, the optimum level of built parameters is identified and validated by a confirmative test predicting an average error of ~1.80%. This work is proficient in producing defect-free parts with maximum densification and improved mechanical properties for newly developed 13Ni-400 maraging steel by the selective laser melting (SLM) technique.

Exploring the Effects of Process Parameters during W/O/W Emulsion Preparation and Supercritical Fluid Extraction on the Protein Encapsulation and Release Properties of PLGA Microspheres.

In this study, protein-loaded poly(lactic-co-glycolic acid) (PLGA) microspheres were prepared via supercritical fluid extraction of emulsion (SFEE) technology. To understand the correlation between process parameters and the main quality characteristics of PLGA microspheres, a comprehensive prior study on the influence of process variables on encapsulation efficiency (EE), initial drug burst release (IBR), morphology, surface property, and particle size distribution (PSD) was conducted within a wide process condition range of each unit process step, from the double-emulsion preparation step to the extraction step. Bovine serum albumin (BSA), a high-molecular weight-protein that is difficult to control the IBR and EE of PLGA microspheres with, was used as a model material. As double-emulsion manufacturing process parameters, the primary (W/O) and secondary emulsion (W/O/W) homogenization speed and secondary emulsification time were evaluated. In addition, the effect of the SFEE process parameters, including the pressure (70-160 bar), temperature (35-65 °C), stirring rate (50-1000 rpm), and flow rate of supercritical carbon dioxide, SC-CO2 (1-40 mL/min), on PLGA microsphere quality properties were also evaluated. An increase in the homogenization speed of the primary emulsion resulted in an increase in EE and a decrease in IBR. In contrast, increasing the secondary emulsification speed resulted in a decrease in EE and an increase in IBR along with a decrease in microsphere size. The insufficient secondary emulsification time resulted in excessive increases in particle size, and excessive durations resulted in decreased EE and increased IBR. Increasing the temperature and pressure of SFEE resulted in an overall increase in particle size, a decrease in EE, and an increase in IBR. It was observed that, at low stirring rates or SC-CO2 flow rates, there was an increase in particle size and SPAN value, while the EE decreased. Overall, when the EE of the prepared microspheres is low, a higher proportion of drugs is distributed on the external surface of the microspheres, resulting in a larger IBR. In conclusion, this study contributes to the scientific understanding of the influence of SFEE process variables on PLGA microspheres.

Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy

Aluminium alloys are gaining popularity in a diversity of engineering applications because of their extraordinary features such as strength, resistance to oxidation, and so on. AA5052 (Al-Mg series) is generally used in antirust uses, particularly in desalination related activities, due to its better resistance to corrosion in marine applications at temperature ranges up to 125°C, lower cost, better heat-carrying capacity, and nontoxicity of its corrosion components. Drilling is one of the most commonly adopted material removal processes that is adopted in numerous engineering uses. Taguchi’s technique is engaged to arrange and examine the tests, by treating the drilling diameter and speed and as independent process factors. The studies were carried out using an L27 orthogonal array. Material removal rate (MRR), surface roughness (SR), and form/orientation error are deemed as output characteristics. Taguchi’s analysis was engaged to discover the best process factors. ANOVA is used to examine the influence of process variables. Suitable application of artificial intelligence tools for making effective decision assists the manufacturer in accomplishing the benefits in numerous engineering domains. To obtain the maximum material removal and minimum roughness, circularity (circ), and perpendicularity errors (perp), the process variables have been optimized with the help of grey-ANFIS-amalgamated with Jaya algorithm. The multiperformance index was developed using grey theory. Statistical error analysis is used to estimate the performance of the established optimization model. Based on the investigative outcomes, the best-suited process variable combinations will be used to provide improved and enhanced multiperformance characteristics.

Dominating influence of cutting-edge lead angles on arc-trajectory machining of Nomex® honeycomb composites by using a straight blade cutter

Nomex® honeycomb composites (NHCs) with circular arc features are a challenging issue in the machining of aviation curved structural components. Compared to linear feature machining, arc feature machining involves six-axis coordinated motion, introducing additional variables. Directly referencing the linear feature machining process will lead to the deterioration of contour accuracy and surface quality in arc-trajectory machining (ATM). To further explore the influence of process variables on ATM, this study developed an error model for straight blade tool (SBT) ATM and investigated the influence of cutting-edge lead angles (γ) on contour accuracy and surface quality. A vertical cutting process was proposed, and experiments proved it provided the optimal machining quality (the defect rate of core cells was only 3.93 %) compared to forward and backward inclination cutting (7.23 %∼34.3 % and 9.71 %∼17.36 %, respectively). Additionally, it was revealed that the main reason for the deterioration of surface quality on ATM was the excessive angle between the cutting edge direction and the feed direction. Based on the results above, the influence and optimization of cutting-edge lead angles were revealed, and the advantage of vertical cutting process for machining NHCs with arc-trajectory was demonstrated. This research provides a feasible method for the machining of NHCs with arc-trajectory using SBT.

EXPLORING DIMENSIONAL PRECISION OF PARAMETRICALLY DESIGNED COMPONENTS USING Z-ULTRAT MATERIAL

In this work, we investigated the dimensional accuracy of parametrized parts in the Catia V5 software. We adopted an experimental plan, applying the Taguchi methodology to evaluate the influence of process variables on dimensional accuracy. Process parameters such as layer thickness, infill percentage, and wall thickness were varied within the experiments, and the obtained results provided essential information regarding the influence of these parameters on the precision of parts obtained through additive manufacturing technology.

Adsorptive removal of oxytetracycline using MnO2-engineered pine-cone biochar: thermodynamic and kinetic investigation and process optimization.

Indiscriminate use of oxytetracycline is linked to the development of antibiotic-resistant genes, posing a serious threat to human health and ecosystem balance. This article reports the adsorptive elimination of oxytetracycline (OTC) from aqueous solution using a newly developed MnO2-modified pine-cone biochar (MnO2/PCBC). The MnO2/PCBC was characterized using X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy, CHNS analyzer, inductively coupled plasma-optical emission spectroscopy, and Brunauer-Emmett-Teller N2 adsorption analyzer. Batch adsorption experiments, designed using the central composite design framework of response surface methodology, were conducted to investigate the influence of process variables on the adsorption of OTC onto MnO2/PCBC. The optimized conditions for achieving maximum removal (88.1%) were found to be at pH 8, MnO2/PCBC dose 0.44 g/L, initial OTC concentration 200 mg/L, and temperature 303 K. The adsorption process follows Langmuir (R2=0.95) and Freundlich (R2=0.95) isotherms and pseudo-second-order (R2=0.99) adsorption kinetics. The adsorption process was found to be endothermic (ΔH0 = 33.04 kJ/mol) and spontaneous in nature (ΔG0 from -1.33 kJ/mol at 283 K to -5.65 kJ/mol at 313 K). The synthesized MnO2/PCBC could be recycled and reused for OTC removal with a percentage removal of around 80% after fifth cycle. The results indicate an effective removal of oxytetracycline with only 0.44 g/L MnO2/PCBC with maximum adsorption capacity of 357.14 mg/g which demonstrates improved performance in comparison to many adsorbents reported in literature. This implies that MnO2/PCBC offers potential to be developed into a cost-effective technique for antibiotic removal from water.

Kinetic evaluation of Pachira aquatica Aubl biomass slow pyrolysis towards to biochar production

The abrupt climate change, caused by the anthropogenic activities in the environment, intensified the search for sustainable sources of energy aiming reduce the dependence on fossil fuels. In this study, a multivariate study design was applied within a batch reactor system to determine the influence of process variables and kinetic parameters of slow pyrolysis of Pachira aquatica Aubl fruit peel towards biochar generation. The statistical evaluation of Box Behnken planning model was applied and showed good adjustment to the model establishing the model that describes the significant effect of variables behavior. The maximum biochar yield (41.22%) was observed were temperature (T) = 406 °C, heating rate β = 2 °C min−1, and residence time t R = 60 min, under nitrogen atmosphere. It is also observed that temperature was the most substantial influence on the process, followed by heating rate and the remaining process variables did not exhibit significant individual effects. The empirical model obtained was applied into mass change equation aiming calculate the activation energy (Ea)=77.10 kJ mol−1 and frequency factor (A 0)= 6.28 × 1010 s−1. The low activation energy in maximum biochar yield region showed a great potential of Pachira aquatica Aubl fruit peel thermoconversion towards to biochar.

Valorization of lignocellulosic waste (coconut coir) for bio-vanillin production having antioxidant and anticancer activity against human breast cancer cells (MCF-7)

The usage of agro-waste for producing natural flavor has gained research interest. Vanillin is a vital flavoring compound widely used in food industries. In the present study, Bacillus aryabhattai NCIM 5503 efficiently utilizes the temple waste coconut coir as the sole carbon source for bio-vanillin production. The most influential process variables, i.e., temperature, pH, and trace metal solution, were identified based on central composite rotary design. Response surface methodology was employed to optimize these process variables, and an enhanced bio-vanillin yield (14.78 %) was obtained. The result demonstrated that a yield of 0.528 ± 0.04 g/100 g bio-vanillin was obtained under optimum conditions (temperature 30 ℃; pH 9; trace metal solution 1.27 mL) after 72 h of fermentation. The predicted response was experimentally validated, resulting in a maximum bio-vanillin of 0.533 ± 0.03 g/100 g. High-performance thin-layer chromatography (HPTLC) of extracted bio-flavor showed a distinct fingerprinting profile similar to bio-vanillin. The bio-flavor produced by B. aryabhattai was characterized as bio-vanillin by FTIR and GC-MS. The antioxidant and anticancer potential of bio-vanillin were also studied. The result revealed that the extracted bio-vanillin has anticancer activity against human breast cancer cells (MCF-7). The experimental results showed the feasibility of SSF as an alternative for producing bio-vanillin through agro-waste valorization.