Production Efficiency Research Articles

Investigation of Arc Stability in Wire Arc Additive Manufacturing of 2319 Aluminum Alloy.

Wire Arc Additive Manufacturing (WAAM) technology, known for its low equipment and material costs, high material utilization, and high production efficiency, has found extensive applications in the fabrication of key components for the aerospace and aviation industries. The stability of the arc is crucial for the WAAM process as it directly affects the forming of the parts. In this study, the monitoring data of electrical signals and arc morphology during the WAAM process of 2319 aluminum alloy were investigated using a high-speed camera system and current/voltage acquisition system. By analyzing the current and voltage signals, as well as the arc imaging results, the influence of arc stability on the forming of the cladding layer was studied. The experimental results indicated that when both current and voltage exhibit regular periodic fluctuations, this manifests as a stable short-circuit droplet transition form, while sudden changes in these signals represent abnormal droplet transition forms. The adaptability of the process directly influenced the arc shape, thereby affecting the forming of the cladding layer. Under the process parameters of welding speed of 240 cm/min and wire feeding speed of 6.5 m/min, it was observed that the current signal exhibited a tight state and the variance of the arc width was minimized. This indicated that at a higher wire feeding speed, the droplet transfer frequency was increased. Under these process parameters, the arc output was more stable, resulting in a uniform metal coating.

Evaluating Maize Residue Cover Using Machine Learning and Remote Sensing in the Meadow Soil Region of Northeast China

The management of crop residues in farmland is crucial for increasing soil organic matter and reducing soil erosion. Identifying the regional extent of crop residue cover (CRC) is vital for implementing conservation tillage and formulating agricultural subsidy policies. The Google Earth Engine (GEE) and remote sensing images from 2019 to 2023 were used to obtain spectral characteristics before the maize seedling stage in Northeast China, followed by constructing the CRC estimation models using machine learning algorithms. To avoid the impact of multicollinearity among data, three machine learning algorithms—ridge regression (RR), partial least squares regression (PLSR), and least absolute shrinkage and selection operator (LASSO)—were employed. By comparing the accuracy of these methods, the most accurate model was determined and applied to subsequent CRC estimation. Based on the estimated CRC and Conservation Technology Information Center definitions of tillage practices, the conservation tillage mapping was completed, and the spatiotemporal distribution characteristics were thoroughly analyzed. The following findings were demonstrated: (1) the PLSR-based model outperformed RR (Pearson’s correlation coefficient (r) = 0.8875, R2 = 0.7877, RMSE = 6.99%) and LASSO (r = 0.8903, R2 = 0.7926, RMSE = 6.88%) with higher accuracy (r = 0.9264, R2 = 0.8582, RMSE = 4.93%). (2) Over the five years, the average no-tillage (NT) proportion in the study area was 15.9%, reduced tillage (RT) was 17.8%, and conventional tillage (CT) was 66.3%. In 2020 and 2022, NT rates were significantly higher at 27.5% and 15.5%, while RT were 15.7% and 30.0%, respectively. (3) Compared to the Sanjiang and Liaohe Plains (RT = 1907 km2 and 1336 km2, and NT = 559 km2 and 585 km2, respectively), the Songnen Plain exhibited higher conservation tillage rates (where RT was 3791 km2 and NT was 1265 km2). This provides crucial scientific evidence for the management and planning of conservation tillage, thereby optimizing farmland production planning, enhancing production efficiency, and promoting the development of sustainable agricultural production systems.

Simulation Experiment Research on the Production of Large Box Girders

This paper introduces system simulation technology into large-scale beam field production and uses the simulation software Arena (14.0) to construct a simulation model of the beam field production system considering the randomness of the actual beam field production process operation time. The relationship between the production efficiency of the beam yard and the working time system was studied. In this paper, the improvement in beam-making efficiency in the existing beam field that is achieved by the commonly used reinforcement pre-binding method in the existing beam field is analyzed and calculated, and the improvements in the production efficiency in the ordinary beam field and the intelligent beam field are quantitatively calculated and compared. The results show that (1) when the working time system is increased from 8 h/d to 12 h/d, the average occupancy time of the traditional beam-making pedestal is shortened by 11.5 h when the working time is extended by 1 h per day; (2) with the extension of the working time system, the advantages of the pre-binding method of reinforcement gradually decrease; and (3) the application of intelligent technology not only improves the production efficiency of the beam yard but also makes the beam yard’s production more flexible and more resistant to risks.

Ferrates (VI) ; Alternative products in water treatment : their electrochemical synthesis

This study focuses on determining the optimum conditions for the electrochemical generation of ferrates in concentrated NaOH solution. Various operating parameters, including electrolyte concentration, current density, temperature, electrolysis duration, and the composition and form of the anode, were investigated to enhance the yield of electrochemical ferrate (VI) ion production. The synthesized ferrates were characterized using UV-visible and FTIR spectroscopic techniques. Experimental results confirmed the formation of ferrate ions, with optimal synthesis conditions identified as follows: a current density of 11.96 mA/cm², NaOH concentration of 16 M, electrolysis time of 1 hour, temperature of 25°C, and the use of white cast iron as the anode material. These conditions were found to yield the highest efficiency for sodium ferrate production. The spectroscopic analysis effectively verified the presence of ferrate (VI) ions, providing valuable insights into the electrochemical synthesis process. This study contributes to the advancement of ferrate synthesis techniques, potentially offering an efficient route for large-scale production.

Construction of pyrrolo[3,2-b]pyrrolyl-linked covalent organic polymers to promote continuous overall H2O2 production

Sacrificial agent-free, solar-driven photocatalytic oxygen reduction holds promise for hydrogen peroxide production. However, the rapid recombination of electron–hole pairs in catalysts and the slow diffusion rate of oxygen on the catalyst surfaces significantly hinder the efficiency of hydrogen peroxide production. To address these issues, we developed a novel class of pyrrolo[3,2-b]pyrrolyl-linked covalent organic polymers (COPs) combined with microreactor technology to enhance hydrogen peroxide synthesis. These structurally well-defined polymers feature photoactive pyrrolo[3,2-b]pyrrolyl units were assembled from aldehyde, aniline, and 2,3-butanedione through a one-pot three-component reaction. Integrating pyrrolo[3,2-b]pyrrolyl moieties into the COPs creates donor–acceptor structures that facilitate the separation of photogenerated electrons and holes. Among them, COP-2 achieved the highest H2O2 yield of 5446 μmol g−1 h−1. Furthermore, we designed a coiled tube photomicroreactor to improve mass transfer efficiency in the gas–liquid–solid triphase system, boosting the H2O2 yield to 20285 μmol g−1h−1 without the need for a sacrificial agent. This study offers new insights into integrating polymer photocatalysts with microflow technology, underscoring the potential for future green and continuous H2O2 production.

Investigating the factors affecting sustainability of ready mix concrete plants: case study of Pune region

Construction industry plays a significant role in the economic development of the nation. Due to site and other constraints, concreting is preferred by ready mix concrete (RMC) plants. It is very well-known fact that the construction industry immensely contributed for CO2 emission mainly from cement-based materials. One of the way to achieve smart city objective to effectively manage ready mix concrete plants. While opting for RMC over site mixing offers various advantages. In this study, key factors governing the sustainability and energy consumption of RMC plants are identified. This study investigates the efficiency and sustainability of RMC production in Maharashtra, suggesting that its efficiency hinges on various factors. Through a constructivist perspective, the research examines twelve RMC plants in Pune mainly and other parts of Maharashtra, finding that power supply, plant output, and capacity require sustainability. While raw material supply is deemed sustainable, management methods and product control were found lacking. The study identifies challenges affecting demand, such as regulatory issues, management issues and technological limitations. The conclusion emphasizes the need for alternative and sustainable power sources, skilled labor, and improved business processes to enhance RMC production efficiency in Maharashtra, recommending measures like waste reuse and innovative advancements. This study will help the stakeholders in prioritizing the resources and process towards more sustainable way.

Design and Optimization of a Chemical Looping Hydrogen Production System Using Steam Reforming for Petrochemical Plants.

Industrial pollution is a significant environmental concern, and researchers are seeking solutions to minimize its impact. Chemical looping combustion is an efficient technique to decrease CO2 emissions. This study delves into its application within petrochemical plants for hydrogen production. In addition to its high efficiency in hydrogen production, this method yields pure CO2 and nitrogen. The design features four reactors utilizing iron and nickel metals as oxygen carriers. A model in Aspen Plus was developed to simulate this design and evaluate its performance. Aspen Plus uses RGIBBS submodel which works based on minimization of the Gibbs free energy. The research findings indicate that this design significantly boosted the hydrogen production rate from 3380 kmol/h in conventional steam reforming reactors and 4258 kmol/h in the three-reactor chemical looping combustion setup to 4408 kmol/h. Lowering the temperatures of the iron and nickel fuel reactors, as well as the iron steam reactor, reducing the airflow rate, and increasing the pressure of the iron steam reactor all led to increased hydrogen production. Conversely, changing the pressure of the nickel fuel reactor had minimal impact. The inlet steam flow rate exhibited a nonlinear effect, initially increasing and then decreasing the hydrogen production rate.

Study of Probiotics as Biostimulants and Biofortifiers in Seed Germination

Germination is a bioprocessing technique used by the food industry to improve the nutrient content of seeds. Imbibition is considered a crucial stage for optimal plant development. In this research, four different groups of seeds were studied to evaluate the effect of using probiotics during the imbibition phase on the germination process, as well as on the microbiological quality, as specified by Mexican regulations. The efficiency of probiotic production was also determined. Regarding the results, most seeds achieved a germination rate between 90–100%, with seeds inoculated with probiotics reaching a higher rate. This increase was related to the concentration of auxins. Importantly, the lowest levels of CFU/g counts for coliforms and fungi were found in the treated sprouts. Additionally, the sprouts exhibited values greater than 1 × 106 CFU/g of probiotics. The imbibition of certain types of seeds with probiotics with Lactiplantibacillus plantarum and Saccharomyces boulardii increases the germination speed, as well as the biomass obtained, which suggests that it may be an alternative to treatments in the agricultural industry for greater production.

Extreme ionizing properties of a metal-poor, M_UV ~ -12 star complex in the first gigayear

We report the serendipitous discovery of a faint (M$_ UV > -12.2$), low-metallicity (Z $ odot $) ionizing source, dubbed T2c, with a spectroscopic redshift of z$=$6.146. T2c is part of a larger structure amplified by the Hubble Frontier Field galaxy cluster MACSJ0416 and was observed with the James Webb Space Telescope ( NIRSpec integral field unit. Stacking the short-wavelength NIRCam data reveals no stellar continuum detection down to a magnitude limit of UV However, prominent and emissions are detected, with equivalent widths exceeding $200$ and $1300$ respectively. The corresponding intrinsic (magnification-corrected $ 3$) ultraviolet and optical rest-frame magnitudes exceed 34.4 and 33.9 (corresponding to UV $ and M$_ opt $ fainter than -12.2 and -12.8 at $ rest and $ respectively), suggesting a stellar mass lower than a few $10^4$ under an instantaneous burst scenario. The inferred ionizing photon production efficiency ($ ion $) is high: $ ion assuming no dust attenuation and no Lyman continuum leakage. This indicates the presence of massive stars despite the low mass of the object. The very poor sampling of the initial mass function in such a low-mass star-forming complex suggests that the formation of very massive stars might be favored in very low-metallicity environments. T2c is surrounded by Balmer and weak oxygen emission on a spatial scale of a few hundred parsecs, after correcting for lensing effects. This system resembles a region potentially powered by currently undetected, extremely efficient, low-metallicity star complexes or clusters. We propose that massive O-type stars populate these low-mass, low-metallicity, high-redshift satellites, likely observed in an early and short formation phase, and contribute to the ionization of the surrounding medium.

Comparative study on slaughter performance, meat quality, and rumen microbiota of Hainan Dong goat and its hybrid with Nubian goat.

The Hainan Dong goat (DG) is a local meat breed widely raised in Hainan, China because of its good adaptability to local hot and humid weather. However, the growth rate of these DG is much slower than that of commercialised breeds improved in European countries, resulting in poor carcase characteristics, including smaller slaughter weight and carcase weight, which have become increasingly prominent. In recent decades, Nubian goats have been continuously imported into China to improve the production performance of local breeds. In this study, the effects of breed on growth performance, carcase and meat quality, and ruminal microbiota were analysed in 40 goats, including 20 DGs and 20 hybrid F3 offspring generated by crossing the DG and Nubian hybrids (NH). All the goats were averagely aged 90 days and weighed at 11 ± 1.34 kg. They were assigned to two treatments, with three replicates per treatment. The two groups were fed the same diet for 90 days before slaughter. The results showed that the average daily gain, F/G ratio, slaughter weight, and carcase weight of the NH group were higher than those of the DG group (p < 0.05). However, tube circumference, meat-to-bone ratio, and eye muscle pH were lower in the NH group than in the DG group (p < 0.05). The NH group showed a smaller fibre crosssectional area and fibre diameter, but a larger fibre density than the DG group (p < 0.05). Bacteroidetes and Firmicutes were the most dominant phyla in the two groups; however, the two breeds had different ruminal microbial communities. In the present study, the differences in growth performance between two groups of goats under the same feeding environment and feed conditions were compared. The correlation between feed sources and rumen flora has been demonstrated, and the results of this study show that the same diet has similar effects on rumen microorganisms, which in turn have related effects on growth and production performance. In summary, hybrids can improve the growth and slaughter performance of local breeds, which may be related to changes in the rumen microorganisms. This study revealed that crossbreeding of Nubian goats with Dong goats has the potential to be used in a wide range of applications owing to its effectiveness in increasing production efficiency.

Effects of Ultrasound on Reactive Crystallization and Particle Properties of an Aromatic Amine in Batch and Continuous modes

Ultrasound has shown its benefits in the manufacturing processes of many pharmaceuticals and fine chemicals. This study focused on the reactive crystallization system of an aromatic amine and explored the potential uses of ultrasound in both batch and continuous modes. In batch experiments, we studied the effects of different sonication conditions including power, duration, and starting point on final particle properties. Under ultrasound, the crystal form and crystal morphology remained well maintained. The results of particle size and size distribution suggested that ultrasound reduced the mean sizes by improving the nucleation process and breaking up large particles. Additionally, the presence of ultrasound in continuous experiments was capable of inducing nucleation and the crystal products collected had a suitable distribution. Integrating ultrasound into the beginning of the continuous crystallization process can be an alternative to the seeding technique. The increasing sonication power did not reduce the induction time substantially. This indicated that a rational sonication condition should balance the overall process efficiency and energy consumption. The findings from batch and continuous experiments indicate that ultrasound could intensify industrial crystallization of the aromatic amine. Incorporating energy-efficient ultrasound with the continuous process will potentially lead to increased production efficiency and a well-controlled product quality.

A rapid-convergent particle swarm optimization approach for multiscale design of high-permeance seawater reverse osmosis systems

Directly solving sophisticated partial differential equation constrained optimization problems is not only extremely time-consuming, but also very hard to find unique optimal solutions. Here, we propose stable and efficient surrogate models for seawater reverse osmosis desalination processes that enable thorough quantitative description of hydrodynamics and local transport characteristics in narrow flow channels. Without iteratively solving complex multi-physics simulation problem taking several hours, the proposed multi-scale design optimization framework significantly reduces the problem complexity by computing the surrogate models in seconds. Moreover, a fast-converging active subspace particle swarm optimization framework is proposed to address the optimal design problem. Compared to the standard particle swarm optimization algorithm, the proposed method enhances the average optimum by 14% and the standard deviation of optimum results for multiple runs is reduced by no less than ten times. The optimized desalination system achieves 9% reduction on energy consumption and 30% improvement on water production efficiency.

An Echo State Network-Based Light Framework for Online Anomaly Detection: An Approach to Using AI at the Edge

Production efficiency is used to determine the best conditions for manufacturing goods at the lowest possible unit cost. When achieved, production efficiency leads to increased revenues for the manufacturer, enhanced employee safety, and a satisfied customer base. Production efficiency not only measures the amount of resources that are needed for production but also considers the productivity levels and the state of the production lines. In this context, online anomaly detection (AD) is an important tool for maintaining the reliability of the production ecosystem. With advancements in artificial intelligence and the growing significance of identifying and mitigating anomalies across different fields, approaches based on artificial neural networks facilitate the recognition of intricate types of anomalies by taking into account both temporal and contextual attributes. In this paper, a lightweight framework based on the Echo State Network (ESN) model running at the edge is introduced for online AD. Compared to other AD methods, such as Long Short-Term Memory (LSTM), it achieves superior precision, accuracy, and recall metrics while reducing training time, CO2 emissions, and the need for high computational resources. The preliminary evaluation of the proposed solution was conducted using a low-resource computing device at the edge of the real production machine through an Industrial Internet of Things (IIoT) smart meter module. The machine used to test the proposed solution was provided by the Italian company SIFIM Srl, which manufactures filter mats for industrial kitchens. Experimental results demonstrate the feasibility of developing an AD method that achieves high accuracy, with the ESN-based framework reaching 85% compared to 80.88% for the LSTM-based model. Furthermore, this method requires minimal hardware resources, with a training time of 9.5 s compared to 2.100 s for the other model.

Specific features of production and use of transportation biofuels in selected developing countries (part II)

Aim. To identify specific features of production and use of transportation biofuels in selected developing countries, as well as to assess the potential of bioethanol industry development in the Russian Federation (RF).Objectives. To assess the economic efficiency of bioethanol production in Russia; to reveal the potential of applying foreign experience in the development of bioethanol industry in our country.Methods. In the process of the research the methods of system analysis, comparative analysis, method of expert evaluations, mathematical, statistical methods were applied.Results. The actual estimation of economic efficiency of bioethanol production in Russia was obtained. The cost of a unit of bioethanol, which is equivalent to a liter of gasoline in terms of energy content, is 5-36% higher than the cost of a liter of gasoline (depending on the production technology). At the same time, state support for the production and use of bioethanol in our country is reasonable. This will allow to create additional demand for grain crops and reduce carbon dioxide emissions from motor transport. The production and use of bioethanol will form an additional supply in the gasoline market, will give an opportunity to support projects on deep processing of grain and wood chemistry.Conclusions. The experience of Brazil, China and Indonesia in the development of bioethanol industry is relevant for Russia. At the same time, in order to minimize possible risks in the field of food security at the initial stage of bioethanol industry development, low-quality grain stocks should be used as feedstock and the production of second-generation bioethanol should be stimulated.

Consistency regularization based semi-supervised plant disease recognition

Plant diseases in agriculture are one of the most important factors affecting the quality and efficiency of production. Autonomous early detection of diseases in plants allows the necessary pesticide work to be carried out on time. Such an autonomous system can minimize production losses and improve product quality. This study highlights the data collection and labeling challenges in the field of plant diseases and proposes to solve the plant disease recognition problem with semi-supervised learning. To this end, we first evaluate three open datasets in the literature and settle on one. Then, numerous experiments are conducted to show that the supervised learning solution to the plant disease recognition problem is ineffective with limited labels. With these experiments, the effects of factors are measured, including limited labeled data, dataset balance, batch size, different fine-tuning strategies, and different levels of input augmentation in supervised learning. Finally, semi-supervised learning experiments showed an improvement in accuracy of up to 5.52 % over supervised learning.

Predictive Modeling and Optimization of Hot Forging Parameters for AISI 1045 Ball Joints Using Taguchi Methodology and Finite Element Analysis

This study focused on optimizing the hot forging process for AISI 1045 medium carbon steel ball joints, which is crucial for enhancing both their mechanical properties and production efficiency. Traditional hot forging processes often face challenges due to variations in flow stress and microstructural outcomes, which can result in a suboptimal product performance. To address these challenges, this research employed the Taguchi method in conjunction with a finite element (FE) simulation to identify the optimal forging parameters. The Arrhenius constitutive model, based on the Zener–Hollomon parameter, was applied to predict the flow stress with a high level of accuracy, achieving a coefficient of determination (R2) of 0.968 and an average absolute relative error (AARE) of 7.079%. An analysis of variance (ANOVA), a statistical innovation that partitions the total variation into components linked to key process factors, was utilized to determine the significance of these parameters. The ANOVA revealed that the billet temperature played a significant role in influencing the preforming force, finishing force, and mean stress, with a maximum impact of 62.30%, 59.50%, and 94.20% on the variation in the response variable, respectively. Additionally, the friction factor significantly affected the preforming and finishing forces, contributing 36.19% and 38.28%. The validation of the model through both simulations and practical experiments is a testament to the reliability of this research, demonstrating the accuracy of the model with minimal discrepancies in the forging forces and exhibiting errors of just 2.88% and 3.40%. Furthermore, microstructure modeling successfully predicted the key outcomes, such as the grain size and pearlite volume fraction, validating the effectiveness of the simulation in forecasting microstructural characteristics.

Maintenance Management Optimization: Evaluating Manual and Automated Methods of Tracking Uptime Hours for Offshore Equipment

Offshore oil and gas fields are among the largest contributors to global oil and gas production. The reliable functioning of these facilities relies heavily on the reliability of the process equipment. Managing and operating offshore equipment is inherently complex, requiring careful planning to ensure maximum uptime and minimal downtime. This study addresses a significant gap in knowledge by comparing the effectiveness of manual and automated methods for tracking the uptime hours of offshore equipment. Using a mixed-methods approach that incorporates quantitative and comparative data analysis, the research assesses the impact of each tracking method on equipment reliability, maintenance costs, and production efficiency. The findings indicate that both methods exhibit high level of accuracy and consistency, with minor differences, confirming the reliability of both methods. However, the study reveals that the automated tracking system, which demonstrated more significant data consistency across shifts when compared to the manual approach, offers substantial advantages, including reduced human error, improved data collection efficiency, and enhanced maintenance scheduling. These benefits, result in improved equipment reliability, lower maintenance costs, optimized maintenance management and greater production efficiency. The study recommends the use of automated logging systems, regular calibration, and maintenance of automated systems, and combining data from both approaches for cross-verification. To ensure accuracy and consistency across all equipment and shifts, it is advised that personnel involved in manual logging receive enhanced training and that logging procedures undergo continuous improvement.

Synergistic Interactions in a Heterobimetallic Ce(III)-Ni(II) Diimine Complex: Enhancing the Electrocatalytic Efficiency for CO2 Reduction.

In this study, we propose a practical approach for producing a heterobimetallic Ni(II)-Ce(III) diimine complex from an extended salen-type ligand (H2L) to serve as an electrocatalyst for CO2 reduction and demonstrate an outstanding overall efficiency of 99.6% of the cerium-nickel complex and integrate it into applicable cell assemblies. We optimize not only the catalyst, but the operational conditions enabling successful CO2 electrolysis over extended periods at different current densities. A comparison of electrochemical behavior in H-cell and zero-gap cell electrolyzers suggests potential applications for industrial scale-up. In the H-cell electrolyzer configuration, the most elevated efficiency in CO production was achieved with a selectivity of 56.96% at -1.01 V vs RHE, while HCOO- formation exhibited a selectivity of 32.24% at -1.11 V vs RHE. The highest TON was determined to be 14657.0 for CO formation, followed by HCOO- with a TON of 927.8 at -1.11 V vs RHE. In the zero-gap electrolyzer configuration, the most efficient setup toward CO production was identified at a current density (CD) of 75 mA cm-2, a flow rate of 10 mL min-1, operating at 60 °C and utilizing a low KOH concentration of 0.1 M to yield a maximum faradaic efficiency (FECO) of 82.1% during 24 h of stable electrocatalysis.

New hybrid system of PV/T, solar collectors, PEM electrolyzer, and HDH for hydrogen and freshwater production: Seasonal performance investigation

A new hybrid energy system of PV/T, flat plate collector, PEM electrolyzer, and humidification-dehumidification desalination is introduced here. A comprehensive mathematical model for the system seasonal performance is constructed and solved numerically using MATLAB software. A year-round comparison between the traditional PV/T performance and its concentrated counterpart indicates that the latter performs better compared to power, hydrogen, and freshwater production and system overall efficiency. The maximum monthly achieved PV/T efficiency, electrolyzer efficiency, desalination system Gain Output Ratio, and overall system efficiency are 52 %, 62 %, 4.8 %, and 44 %, respectively at a concentration ratio of 4 and maximum allowable PV temperature. The system produces 82,200 kWh of energy annually and provides 77,700 kWh and 1500 kWh to the electrolyzer and the electric heater, respectively. Moreover, the system produces an annual freshwater production of about 52,700 kg, out of which approximately 10,000 kg are consumed in the electrolysis process, and an annual hydrogen production of approximately 1120 kg.

Effect of a Bacillus subtilis-based direct-fed microbial, on milk yield, milk components, feed intake and plasma hormones and metabolites in lactating Holstein cows

To investigate the effects of a Bacillus-based direct-fed microbial on milk production and related factors, a study was conducted on Holstein cows (n = 28) starting at 90 ± 11 DIM. The cows were divided into two dietary groups: a control group (CON) that received a total mixed ration (TMR), and a Bacillus-fed group (DFM) that received the same TMR along with a Bacillus subtilis product containing two strains (747 and 1781) in equal amounts. The study lasted for 25 wk and included both primiparous and multiparous cows. The cows were housed in a free-stall barn and were provided with ad libitum TMR, which was fed twice a day. Their daily meals and dry matter intake (DMI) were recorded during wk 1–4 and wk 19–25 using electronic feeders. Milk samples were collected weekly during morning and evening milkings and analyzed for milk fat, protein, lactose, and milk urea nitrogen (MUN). Blood samples were also collected weekly and analyzed for plasma glucose, insulin, cholesterol, insulin-like growth factor 1 (IGF1), and progesterone concentrations. Additionally, rumen fluid samples were collected and evaluated for various species of bacteria. One of the key findings was that daily 4 % fat-corrected milk production (FCM) was influenced by the interaction between treatment (DFM vs. CON) and parity (primiparous vs. multiparous), with multiparous DFM cows producing 11 % more FCM compared to CON. The DFM cows also had a higher milk fat percentage (4.41 % vs. 4.02 %) and MUN concentrations were 1.0 mg/dL higher in the DFM group. In terms of blood metabolites and hormones, multiparous DFM cows had lower plasma cholesterol, glucose, and insulin concentrations compared to CON multiparous cows. However, there were no significant differences between the groups in terms of plasma IGF1 concentrations, insulin-to-glucose ratios, and luteal phase plasma progesterone concentrations. The analysis of rumen fluid samples revealed that the abundance of Ruminococcus albus and Fibrobacter succinogenes group I was greater in the DFM cows compared to CON cows. Furthermore, during the periods when feed intake was measured, the DFM cows had a 9 % reduction in feed intake and a 14 % improvement in feed efficiency (FCM per DMI) compared to CON cows. In conclusion, the two-strain Bacillus product used in this study (Certillus) showed potential as an effective direct-fed microbial. It was found to increase feed efficiency and milk production by altering the composition of ruminal microbiota and metabolism in the cows.