Production Process Control Research Articles

Metode Pelaksanaan Pembuatan PC Spun Pile dengan Mutu F'c 52 MPa Pada PT. Wijaya Karya Beton Tbk PPB Majalengka

A concrete plant, also known as a batch plant or batching plant or a concrete batching plant, is equipment that combines various ingredients to form concrete. This implementation aims to determine the manufacturing process and quality control of concrete product production. The method used in this implementation is descriptive quantitative. Steps include operational variables to determine the problem and indicators of the cause of the problem. Data collection through observation and documentation. The results of this implementation show that the implementation of manufacturing at PT WIKA Beton Tbk PPB Majalengka aims to make good concrete products, the factors for making PC Spun Pile products are made based on work instructions or standard operational procedures.

Operation prediction of open sun drying based on mathematical-physical model, drying kinetics and machine learning

In order to determine the moisture ratio of dried material whether the storage requirements are met, it was crucial to find an accurate prediction and convenient method in the open sun drying process. Therefore, the mathematical-physical model, drying dynamics model and machine learning methods were employed and compared in this study. The machine learning methods were first applied to predict the moisture ratio change of sweet potato during open sun drying. A large number of sweet potatoes drying experiments were carried out under open sun drying for theoretical analysis. The results shown that the drying kinetic model of sweet potato was also different under different drying climate conditions. The heat and mass transfer model of sweet potato was established and validated with R2 0.8990 and RMSE 0.0826. Different optimal machine learning prediction methods have be selected based on statistical metrics. Finaly, the machine learning prediction method was considered to be superior to the mathematical-physical model and the drying kinetic model in predicting moisture ratio. The results of this study can be analogized to drying process control of other agricultural products in the future.

Miniature optical fiber photoacoustic spectroscopy gas sensor based on a 3D micro-printed planar-spiral spring optomechanical resonator

Photoacoustic spectroscopy (PAS) gas sensors based on optomechanical resonators (OMRs) have garnered significant attention for ultrasensitive trace-gas detection. However, a major challenge lies in balancing small size with high performance when developing ultrasensitive miniaturized optomechanical resonant PAS (OMR-PAS) gas sensors for space-constrained applications. Here, we present a miniature optical fiber PAS gas sensor based on a planar-spiral spring OMR (PSS-OMR) that is in situ 3D micro-printed on the end-face of a fiber-optic ferrule. Experimental results demonstrate that mechanical vibrational resonance can enhance the sensor's acoustic sensitivity by over two orders of magnitude. Together with a 1.4 μL non-resonant photoacoustic cell, it can detect C2H2 gas concentration at the 45-ppb level, and its response is very fast approximating 0.2seconds. This optical fiber OMR-PAS gas sensor holds great promise for the detection or monitoring of rapidly varying trace gas in many applications ranging from production process control to industrial environmental surveillance.

Research Status and Safety Considerations of Animal-Derived Mesh Products

With the development of the economy and technological progress, more and more animal-derived mesh products are being utilized in the medical field for tissue and organ repair and replacement. Owing to the complexity of their structure and production process, these animal-derived meshes still face several challenges in practical applications, such as insufficient mechanical strength, rapid degradation rates, and the detection of harmful leachable substances. Among these challenges, the production process is a key factor affecting product quality. This paper reviews the key aspects of the production process and quality control for animal-derived meshes in China, offering new insights for the quality control and regulatory oversight of such products.

Biochars from chlorine-rich feedstock are low in polychlorinated dioxins, furans and biphenyls

Chlorinated aromatic hydrocarbons like polychlorinated dibenzo-p-dioxins and -furans (PCDD/F) and polychlorinated biphenyls (PCB) are omnipresent in the environment due to historic production, use, and (unintended) release. Nowadays, their emission and maximum concentration in environmental compartments is strictly regulated. During biochar production, PCDD/F and PCB may be formed and retained on the solid pyrolysis product. Industrial biochars certified, e.g., under the European Biochar Certificate (EBC), exhibit concentrations that were always well below threshold values for soil application and even animal feed. However, this has not been sufficiently tested for chlorine (Cl) rich organic material such as marine biomass or polyvinyl chloride (PVC) contaminated feedstock. Here, we analyzed PCDD/F and PCB contamination in biochars produced at different temperatures from different biomasses with comparatively high Cl contents in the range from 0.2 % to 3.8 % (w/w, seagrass, two types of saltwater macroalgae, tobacco stalks, and PVC contaminated wood). All of the biochars produced showed PCDD/F and PCB contents well below the applicable threshold values given by the EBC (< 20 ng TEQ kg−1 for PCDD/F and < 2×105 ng kg−1 for PCB). The EBC thresholds were undershot by a minimum of factor 1.5 for PCDD/F (mostly factor 20) and by a minimum of factor 90 for PCB. Between 1 and 27 ppb of feedstock Cl were transformed to Cl bound in PCDD/F and PCB in the biochars. No consistent correlation between biomass Cl contents and contents of PCDD/F and PCB were found but higher Cl contents in the feedstock led to a more diverse PCDD/F congener pattern in the biochars. Pyrolysis of PVC-amended wood resulted in consistently higher contamination of PCDD/F and PCB in the biochars compared to pyrolysis of the other biomasses, potentially due to differences in Cl speciation in the feedstocks i.e., Cl in PVC is already covalently bound to an organic carbon backbone. A high contamination in PCDD/F and PCB in biochar was intentionally triggered by separation of pyrogas and biochar in the reactor at < 300 °C to promote condensation of contaminants on the solid product. Between 20 % and 80 % of feedstock Cl was released via the pyrogas, i.e., neutralization of HCl in burnt pyrogas might be necessary when pyrolyzing Cl-rich feedstock in industrial biochar production. Our results indicate that biochars produced from Cl-rich feedstocks with proper biochar production process control are conform with European certification guidelines for PCDD/F and PCB contamination. The results open the opportunity to exploit and valorize so far non-used marine or otherwise Cl enriched biomasses for the production of biochar and carbon sinks.

Integrated optimization for X-bar control chart, preventive maintenance and production rate

There are close relationships among statistical process control, maintenance, and production that have not fully explored in existing studies. In this study, we propose an economic model for jointly optimizing the X-bar control chart, preventive maintenance, and production rate. Unlike conventional studies relying on known failure rate functions, we employ a Gamma process to describe the system's deterioration. This approach allows us to consider not only the relationship between the production rate and the deterioration rate but also a broad range of deterioration scenarios and failure modes in the integrated optimization. After calculating the failure probability at each time point, we propose a recursive method to derive the production revenue, process control costs, and quality losses. This approach explores the close inner relationship between the production rate, deterioration, process monitoring and maintenance. Subsequently, we establish an optimization model aimed at maximizing the profit rate within a renewal cycle. The decision variables of this model include the production rate, control chart coefficient, sample size, length of the first sampling interval, and number of sampling intervals. Finally, we use a case study to illustrate the effectiveness and validity of our proposed model by identifying and explaining the interactions between production rate, deterioration, process control and maintenance.

Engineering advanced cellulosics for enhanced triboelectric performance using biomanufactured proteins

Biological polymers, such as polysaccharides and polypeptides, offer renewable and biodegradable solutions for a more sustainable future. These polymers comprise natural building blocks, such as amino acids and glycans, which ensure their true environmental benefits at the end of their lifecycle. For example, cellulose is a highly sustainable material with many excellent properties, including renewability, biodegradability, and versatility in its functionality. It can be used in various forms, such as textiles, packaging materials, and building insulation. Here, we studied advanced cellulosic materials produced by blending or creating bi-composites with biomanufactured proteins inspired by squid ring teeth (SRT). Biomanufactured proteins can be synthesized in larger quantities, have a controlled production process, be modified to create desirable variants, and their production can be scaled up or down. Specifically, we engineered recombinant SRT proteins to have high electrostatic charge, induce crystallinity, and provide polar hydroxyl groups, which enhances cellulosic materials’ triboelectric response. The triboelectric voltage of blend triacetate and cellulose fibers increased by 72–108% and 49–57%, respectively, with a protein content of 10% wt. Furthermore, coating proteins on cellulosic fibers to create bi-composite fibers is a highly effective method for doubling (200%) the triboelectric performance. This finding has important implications for developing sustainable triboelectric materials and producing advanced materials using biomanufacturing.

Steady-state and dynamic simulation of gas phase polyethylene process

Gas-phase polyethylene (PE) processes are among the most important methods for PE production. A deeper understanding of the process characteristics and dynamic behavior, such as properties of PE and reactor stability, holds substantial interest for both academic researchers and industries. In this study, both steady-state and dynamic models for a gas-phase polyethylene process are established as a simulation platform, which can be used to study a variety of operation tasks for commercial solution polyethylene processes, such as new product development, process control and real-time optimization. The copolymerization kinetic parameters are fitted by industrial data. Additionally, a multi-reactor series model is developed to characterize the temperature distribution within the fluidized bed reactor. The accuracy in predicting melt index and density of the polymer, and the dynamic behavior of the developed models are verified by real plant data. Moreover, the dynamic simulation platform is applied to compare four practical control schemes for reactor temperature by a series of simulation experiments, since temperature control is important in industrial production. The results reveal that all four schemes effectively track the setpoint temperature. However, only the demineralized water temperature cascade control demonstrates excellent performance in handling disturbances from both the recycle gas subsystem and the heat exchange subsystem.

The Use Of Industry 4.0 Technology In The Transformation Of Production Processes Towards Smart Factories

This research aims to explore the use of Industry 4.0 technology in the transformation of the production process towards a smart factory. The method used in this research is qualitative. The process of collecting data and information comes from literature studies obtained from various articles, books, and documents relevant to the topic of discussion. The results show that the implementation of industry 4.0 technology can improve efficiency, flexibility, and operational resilience in the production environment. Through better integration between equipment and systems, smart factories can monitor, forecast, and control production processes in real-time, allowing adaptation of production models that are responsive to market dynamics. However, there are also challenges and opportunities that arise in its application. As such, this research provides a contribution to understanding the implications of Industry 4.0 technologies on the future of production and industry as a whole.

Transforming Tofu Quality Control: Integrating Statistical Process Control, Ishikawa, and Interpretive Structural Modeling for Superior Outcomes

In a highly competitive market, maintaining high product quality is essential for maintaining customer satisfaction and loyalty. Producers are famous in East Java and face the challenge of ensuring consistent quality products through strict production processes. Know that as easy products broken and soft, needy control of careful quality to meet hope consumers about freshness, texture, and quality in a way whole. Although traditional control methods often fail to overcome the production complex that causes a disabled product. This research proposes integrating Statistical Process Control (SPC), Ishikawa diagrams, and Interpretive Structural Modeling (ISM) to improve control quality. SPC makes it possible to monitor and control production processes in real-time, identify deviations, and repair deviations. However, the SPC limitations include a focus on quantitative data and post-incident detection problems. To overcome root problems, the Ishikawa diagram categorizes the reasons for potency as material, machine, method, power work, and environment. ISM prioritizes action repair based on impact and relationship. Approach This integrated approach provides comprehensive solutions to improve the quality of knowledge. In this study, the control process quality was evaluated using SPC, the cause of defects was identified using Ishikawa diagrams, and priority action repair was performed via ISM. Findings show that SPC directly effectively monitors process control, the Ishikawa diagram identifies main defects, and ISM prioritizes impactful action improvements, emphasizes excessive additions to subtraction materials, and improves health workers’ and mixing process material standards. Approach integrated possible identification of additional problems and improved the quality strategic makes a significant contribution to enhancing product quality knowledge.

A Maturity Model to Determine the Degree of Utilization of Machine Learning in Production Planning and Control Processes

The presented work introduces a maturity model for evaluating Machine Learning implementations, with a primary focus on Production Planning and Control processes, as well as broader organizational and technical aspects in companies. This model emerges as a response to the research gap identified in the analysis of 14 existing maturity models, which served as foundational bases for the development of this novel approach. By examining success factors and obstacles at different maturity levels, categorized according to defined dimensions and overarching design fields, this model can serve as a catalyst for bridging the research gap between models demanded in practice and the scholary exploration of topics related to Machine Learning in corporate processes. Notably, the structured design of this maturity model ensures accessibility for small and medium sized enterprises (SMEs).

Wavelet Cross-Correlation Signal Processing for Two-Phase Flow Control System in Oil Well Production

An algorithm based on continuous measurement of multiphase flows of oil well production has been designed to improve the efficiency of the technical control of oil production processes in the field. Separation-free, non-contact measurement of multiphase flows of oil well products allows increasing the efficiency of managing oil production processes in the field. Monitoring the current density using radioisotope measuring transducers (RMTs) allows obtaining information about the structure of the flow in the form of the distribution of gas inclusions and the speed of movement of liquid and gas in a two-phase flow. Fluid velocity measurement is based on digital processing of RMT signals, applying a continuous or discrete undecimated wavelet transform to them, and assessing the cross-correlation of wavelet coefficients in individual subspaces of the wavelet decomposition. The cross-correlation coefficients of two RMT signals located at a base distance, calculated in the subspaces of the wavelet decomposition, characterize the speed of movement of gas bubbles of different sizes in a vertical pipe. The measurement assumes that the velocity of the liquid phase of the oil flow in a vertical pipe mainly corresponds to the velocity of small bubbles. This speed should be determined by the maximum cross-correlation of wavelet coefficients in the corresponding decomposition subspace. Computer modeling made it possible to evaluate the characteristics of the algorithm for controlling the speed of liquid movement in the gas–liquid flow of oil well products and determine the mass flow rate of the liquid and the relative value of the gas content. The implementation of the algorithm in a multi-channel version of the device allows monitoring an entire cluster of wells in the field.

Advancements in biofuel research: Understanding the physical and chemical characteristics of green diesel

Green diesel is a potential biofuel that offers certain advantages over traditional fossil diesel, such as low environmental pollution and no sulfur. However, there is little information available regarding the density and viscosity of green diesel, which is crucial for the quality control of biofuels and their performance in engines. In this study, the properties of green diesel produced from palm and soybean oils hydrotreatment were investigated, and models were developed to predict the physical properties of green diesel and n-alkanes. The experimental measurements had uncertainties of 1×10-4 g/cm3 and 1×10-2 mPa s in density and viscosity, respectively. The models were found to be in good agreement with the experimental values. The study highlights the importance of density and viscosity for quality control and engine performance of biofuels. The developed models for predicting the physical properties of green diesel and n-alkanes can be useful for quality control and optimization of biofuel production processes. The findings can be useful for researchers, engineers, and policymakers working in the field of biofuels and renewable energy. Further research is needed to explore the applicability of the developed models to other types of biofuels and to investigate the environmental and economic sustainability of green diesel production.

Joint Prescriptive Maintenance and Production Planning and Control Process Simulation for Extrusion System

Production planning and control (PPC) is the mainstay of every manufacturer and ensures flawless production processes. However, PPC is jeopardized by breakdowns that can only be tackled with appropriate maintenance. In the past, static strategies, such as reactive and scheduled maintenance, have been used. Yet, with growing system complexity, Industry 4.0, and abundant sensor data, dynamic strategies through PHM have emerged. The most advanced maintenance strategy is prescriptive maintenance (PxM), which allows manufacturers not only to predict failures but also to establish condition-based production plans and controls. To this end, our study explores the integration of PxM with PPC. First, we propose a fault prediction model based on health indicators and future loads of a single-machine system. The proposed fault prediction is integrated into a joint PxM and PPC simulation model that compares the make­span of three joint PxM and PPC strategies inter se and versus reactive and scheduled maintenance. A simulation study using industrial data from an extrusion process evaluates the different strategies across different time horizons (one month to a year). The findings indicate that joint PxM and PPC outperform other strategies, providing significant time savings over traditional methods. Further, a sensitivity analysis is conducted to assess the robustness of the PxM strategies under varying levels of measurement noise, revealing potential challenges under high noise conditions. The study contributes to the field of PHM by providing insights into the effectiveness of joint PxM and PPC strategies and offering a comprehensive analysis of their performance under different conditions.

PROBLEMS OF ELECTROMAGNETIC SAFETY OF OPERATORS OF PRODUCTION PROCESSES IN THE MACHINERY INDUSTRY

In industrial enterprises, any employee comes into contact with many sources of electric and magnetic fields. The electrical installations around us generate various electromagnetic fields. The work examines the features of the formation of the electromagnetic environment near machine tools. The goal of the work is to ensure safe working conditions for operators and dispatchers of production process control panels based on reducing the impact of the electromagnetic field to standard values.

Evaluation and Analysis of Cement Raw Meal Homogenization Characteristics Based on Simulated Equipment Models.

In recent years, the variability in the composition of cement raw materials has increasingly impacted the quality of cement products. However, there has been relatively little research on the homogenization effects of equipment in the cement production process. Existing studies mainly focus on the primary functions of equipment, such as the grinding efficiency of ball mills, the thermal decomposition in cyclone preheaters, and the thermal decomposition in rotary kilns. This study selected four typical pieces of equipment with significant homogenization functions for an in-depth investigation: ball mills, pneumatic homogenizing silos, cyclone preheaters, and rotary kilns. To assess the homogenization efficacy of each apparatus, scaled-down models of these devices were constructed and subjected to simulated experiments. To improve experimental efficiency and realistically simulate actual production conditions in a laboratory setting, this study used the uniformity of the electrical capacitance of mixed powders instead of compositional uniformity to analyze homogenization effects. The test material in the experiment consisted of a mixture of raw meal from a cement factory with a high dielectric constant and Fe3O4 powder. The parallel plate capacitance method was employed to ascertain the capacitance value of the mixed powder prior to and subsequent to treatment by each equipment model. The fluctuation of the input and output curves was analyzed, and the standard deviation (S), coefficient of variation (R), and homogenization multiplier (H) were calculated in order to evaluate the homogenization effect of each equipment model on the raw meal. The findings of the study indicated that the pneumatic homogenizer exhibited an exemplary homogenization effect, followed by the ball mill. For the ball mill, a higher proportion of small balls in the gradation can significantly enhance the homogenization effect without considering the grinding efficiency. The five-stage cyclone preheater also has a better homogenization effect, while the rotary kiln has a less significant homogenization effect on raw meal. Finally, the raw meal processed by each equipment model was used for clinker calcination and the preparation of cement mortar samples. After curing for three days, the compressive and flexural strengths of the samples were tested, thereby indirectly verifying the homogenization effect of each equipment model on the raw meal. This study helps to understand the homogenization process of raw materials by equipment in cement production and provides certain reference and data support for equipment selection, operation optimization, and quality control in the cement production process.

PENGARUH PROSES PRODUKSI DAN PENGENDALIAN KUALITAS TERHADAP KUALITAS PRODUK YHA DI PT. FURUKAWA AUTOMOTIVE SYSTEM INDONESIA CIKARANG

The quality of a product is one of the most important factors in the industrial world as in PT. Furukawa Automotive System Indonesia. The company always tries to maintain the quality of its products, but inreality things don't go smoothly. Every month there are still a large number of damaged products (NG) both in the production process and in quality control that are not optimal. There fore the purpose of this research is to find out how much influence the production process and quality control have on the quality of the product produced either partially or simultaneously. This study uses a quantitative approach with primary data as a source of data obtained from the results of the questionnaire. Data analysis methods in this study are validity test, reliability test, classic assumption test which consists of normality test, heteroscedasticity test, multicollinearity test, linearity test, hypothesis test and determination test. The results of this study indicate that (1) the production process variable has a positive and significant effect on the product quality of PT. Fasi, (2) the quality control variable has a positive and significant effect on the product quality of PT. Fasi, (3) the production process variables and quality control variables together have an influence and are significant on the product quality of PT. Fasi.

Data-Physics Fusion-Driven Defect Predictions for Titanium Alloy Casing Using Neural Network.

The quality of Ti alloy casing is crucial for the safe and stable operation of aero engines. However, the fluctuation of key process parameters during the investment casting process of titanium alloy casings has a significant influence on the volume and number of porosity defects, and this influence cannot be effectively suppressed at present. Therefore, this paper proposes a strategy to control the influence of process parameters on shrinkage volume and number. This study constructed multiple regression prediction models and neural network prediction models of porosity volume and number for a ZTC4 casing by simulating the gravity investment casting process. The results show that the multiple regression prediction model and neural network prediction model of shrinkage cavity total volume have an accuracy of over 99%. The accuracy of the neural network prediction model is higher than that of the multiple regression model, and the neural network model realizes the accurate prediction of shrinkage defect volume and defect number through pouring temperature, pouring time, and mold shell temperature. The sensitivity degree of casing defects to key process parameters, from high to low, is as follows: pouring temperature, pouring time, and mold temperature. Further optimizing the key process parameter window reduces the influence of process parameter fluctuation on the volume and number of porosity defects in casing castings. This study provides a reference for actual production control process parameters to reduce shrinkage cavity and loose defects.

Zielgerichteter Einsatz von Traceability-Systemen

Abstract Traceability systems record information like component ID, processing station and processing time in production. This allows for example to verify the product origin, to limit recalls and to dynamically control production processes. In small and medium-sized companies, Traceability systems are not comprehensively used. This article describes a maturity model that supports demand-oriented system launches and further developments.

Development and Efficacy Evaluation of Innovative Cosmetic Formulations with Caryocar brasiliense Fruit Pulp Oil Encapsulated in Freeze-Dried Liposomes.

Encapsulation and drying technologies allow the engineering of innovative raw materials from plant biodiversity, with potential applications in pharmaceutical and cosmetic fields. Lipid-based nanoencapsulation stands out for its efficiency, ease of production, and versatility in encapsulating substances, whether hydrophilic or lipophilic. This work aimed at encapsulating pequi oil in liposomes and freeze-dried liposomes to enhance its stability and functional benefits, such as skin hydration and anti-aging effects, for use in innovative cosmetic formulations. Pequi oil-extracted from the Caryocar brasiliense fruit pulp, a plant species from Brazilian plant biodiversity-is rich in secondary metabolites and fatty acids. Liposomes and dried liposomes offer controlled production processes and seamless integration into cosmetic formulations. The physicochemical analysis of the developed liposomes confirmed that the formulations are homogeneous and electrokinetically stable, as evidenced by consistent particle size distribution and zeta potential values, respectively. The gel-type formulations loaded with the dried liposomes exhibit enhanced skin hydration, improved barrier function, and refined microrelief, indicating improvements in skin conditions. These results highlight the potential of dried liposomes containing pequi oil for the development of innovative cosmeceutical products. This research contributes to the valorization of Brazilian biodiversity by presenting an innovative approach to leveraging the dermatological benefits of pequi oil in cosmetic applications.