Production Process Control Research Articles

Towards Flexible Control of Production Processes: A Requirements Analysis for Adaptive Workflow Management and Evaluation of Suitable Process Modeling Languages

In the context of Industry 4.0, Artificial Intelligence (AI) methods are used to maximize the efficiency and flexibility of production processes. The adaptive management of such semantic processes can optimize energy and resource efficiency while providing high reliability, but it depends on the representation type of these models. This paper provides a literature review of current Process Modeling Languages (PMLs). Based on a suitable PML, the flexibility of production processes can be increased. Currently, a common understanding of this process flexibility in the context of adaptive workflow management is missing. Therefore, requirements derived from the business environment are presented for process flexibility. To enable the identification of suitable PLMs, requirements regarding this are also raised. Based on these, the PMLs identified in the literature review are evaluated. Thereby, based on a preselection, a detailed examination of the seven most promising languages is performed, including an example from a real smart factory. As a result, a recommendation is made for the use of BPMN, for which it is presented how it can be enriched with separate semantic information that is suitable for the use of AI planning and, thus, enables flexible control.

MXene-Graphene Oxide Heterostructured Films for Enhanced Metasurface Plasmonic Biosensing in Continuous Glucose Monitoring.

Non-invasive biosensors have attracted attention for their potential to obtain continuous, real-time physiological information through measurements of biochemical markers, such as one of the most important-glucose, in biological fluids. Although some optical sensing materials are used in non-invasivedevices for continuous glucose monitoring (CGM), surface or localized plasmon sensing material are seldom applied in CGM owing to modest sensitivity and bulk sensing apparatus. Herein, a metasurface (MGMSPR) biosensor based on the metasurface plasmon resonance chip modified with heterostructured Ti3C2 MXene-Graphene oxide (MG) is reported, which potentially enables ultra-sensitive glucose detection. The sensor consists of a dual-channel microfluidic device integrated with silver mirror enhanced MGMSPR chips. Not only does it promote the entry of glucose oxidase (GOD) into the internal pores and enhance the stable fixation of GOD in the membrane, but also the integration of MG material provides a high specific surface area and unique electronic properties, thereby significantly enhancing the sensitivity of the MGMSPR sensor. The detection limit of MGMSPR biosensor is 106.8µM. This pioneering approach opens new avenues for monitoring physiological parameters and process analytical technology on an optical platform, providing continuous health monitoring and production process control through optical sensors.

Application of SQC and ANOVA in Quality Control of Palm Kernel Oil

The measurement and control of free fatty acids (FFA), moisture content (MC), and impurity content (IC) using Statistical Quality Control (SQC) in the oil production process were crucial to ensure optimal quality of palm kernel oil (PKO). This study aimed to determine the variance influencing the quality of PKO using Anova analysis. Anova (Analysis of Variance) is a statistical test procedure used to test mean differences among more than two groups. The results of the study indicated that the analysis of variance Fcount values for free fatty acids, moisture content, and impurity content were smaller than the Ftable values, indicating that the measurement results or mean values of these four data groups did not have statistically significant differences.

Artificial Intelligence and/or Machine Learning Algorithms in Microalgae Bioprocesses.

This review examines the increasing application of artificial intelligence (AI) and/or machine learning (ML) in microalgae processes, focusing on their ability to improve production efficiency, yield, and process control. AI/ML technologies are used in various aspects of microalgae processes, such as real-time monitoring, species identification, the optimization of growth conditions, harvesting, and the purification of bioproducts. Commonly employed ML algorithms, including the support vector machine (SVM), genetic algorithm (GA), decision tree (DT), random forest (RF), artificial neural network (ANN), and deep learning (DL), each have unique strengths but also present challenges, such as computational demands, overfitting, and transparency. Despite these hurdles, AI/ML technologies have shown significant improvements in system performance, scalability, and resource efficiency, as well as in cutting costs, minimizing downtime, and reducing environmental impact. However, broader implementations face obstacles, including data availability, model complexity, scalability issues, cybersecurity threats, and regulatory challenges. To address these issues, solutions, such as the use of simulation-based data, modular system designs, and adaptive learning models, have been proposed. This review contributes to the literature by offering a thorough analysis of the practical applications, obstacles, and benefits of AI/ML in microalgae processes, offering critical insights into this fast-evolving field.

Development and Experimental Study of an Experimental Setup for an Online Vibrating Tube Liquid Densitometer

Density is a crucial parameter for quantitatively describing the physical properties of liquids. It serves as an important indicator for scientific research, production process control, pipeline transportation, and other aspects. In oil pipeline transportation and raw material processing, the real-time online measurement of liquid density is of great significance. This paper analyzes the working principle of an online vibrating tube densitometer and derives the fitting equation for temperature, pressure, and density; it also conducts experiments with an online vibrating tube liquid densitometer and establishes a traceability chain for the experimental device. The experimental setup includes a desktop densitometer system, a multi-temperature field constant-temperature stirring system, a walk-in constant-temperature box, an automatic blowing system, and a frequency acquisition and calculation system. The uncertainty of the device’s evaluation is U = 0.08 kg/m3, k = 2. We built a set of pressure-density static test systems, statically testing the online vibrating tube’s liquid-density meter vibration frequency at different pressures; the whole set of systems can be used to assess the specific density, temperature, and pressure range of online vibrating tube liquid density meters in the experimental research to derive the standard temperature. Through the experimental research, we can accurately derive the fitting coefficients under the standard temperature, specific temperature, and pressure of online vibrating tube liquid densitometers, and calculate the fitting error of online vibrating tube liquid densitometers under different temperatures and pressures within the experimental range through fitting equations and coefficients, so as to realize the practical application of online vibrating tube liquid densitometers in engineering by utilizing straight-tube-type and curved-type online vibrating tube densitometers. A preliminary study was conducted on the effects of different densities, temperatures, and pressures on the vibrating tube system’s vibration cycle. The fit coefficient and error were calculated, and the experimental results were compared to the theoretical analysis to confirm the device’s conformity. The study verified the device’s scientific and reasonable design, and demonstrated that it is feasible to use the device for follow-up research. Using this device in subsequent experiments can verify the effects of viscosity, inlet, installation, and other factors on the online vibrating tube liquid densitometer’s metrological performance. Further experimental research on the pressure–frequency–density test system and the establishment of a wide range of temperatures and pressures within the pressure standard density test system are needed to achieve a wide range of temperatures and pressures under the standard density test.

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 0.2 seconds. 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.

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.

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.

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.

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The article explores creative management of business structures' competitiveness during the war and the recovery of Ukraine's economy. It substantiates the importance of implementing innovative solutions such as the Internet of Things (IoT), Artificial Intelligence (AI), and blockchain technologies, which help businesses adapt to challenging conditions and ensure efficient operations. It is found that innovative approaches contribute to optimizing production processes, reducing costs, and improving management practices, which enhances the competitiveness of Ukrainian businesses in both domestic and international markets. The research demonstrates that the use of technologies like IoT for production monitoring enables businesses to control production processes in real-time and reduce operational costs. Warehouse automation and the use of drones in logistics help minimize risks for workers and improve the accuracy of operations, which is crucial during wartime. This illustrates the ability of Ukrainian entrepreneurs to quickly adapt to new challenges and effectively utilize technological innovations to maintain business resilience. However, the implementation of innovations during wartime presents its own risks, including cybersecurity issues, high costs of technology adoption, and infrastructure instability. Despite these challenges, businesses that adopt creative solutions show readiness for change and ensure continuity of operations by employing strategic innovative management approaches. This allows them to function effectively in unstable conditions and lay the groundwork for long-term development. The article concludes that innovative thinking and creative management are key to enhancing the competitiveness of Ukrainian business structures in the challenging conditions of war and recovery. The adoption of the Internet of Things and other innovative solutions is a crucial factor for ensuring the sustainable development of businesses and Ukraine's economy in modern conditions. Keywords: creative management, competitiveness, innovative solutions, Internet of Things (IoT), Artificial Intelligence (AI), blockchain, business structures, Ukraine's economy, war, recovery, automation, risk management, digitalization

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.

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.

NO-CONTACT DIAGNOSTICS IN THE FUNCTION OF RISK-BASED MAINTENANCE ON A WIND GENERATOR MODEL

When it comes to risk-based maintenance of technical systems, selecting a diagnostic procedure is the weakest link in the chain of steps that guarantee the system's availability, sustainability, and safe operation while utilizing its working characteristics.The model of a wind generator, which by virtue of its technological structure represents an increasingly widespread system in the geographical and topographical sense, was used to examine diagnostics based on non-contact, remote collection of pertinent information, which is essential for assessing the state of the technical system. In addition, the fact that the system is not physically accessible to the maintenance teams emphasizes the necessity of taking prompt action to rectify work in order to preserve production capacity and the security of the exploitation process.When applied to the wind generator model, proactive maintenance based on risk allows for enhanced interconnected machine efficiency, optimal maintenance process planning, and real-time production process control. These factors contribute to the advancement and refinement of the contemporary 4K industry philosophy.The concept of remote diagnostics in complex technological structures is of interest to the authors. The goal is to process information about the condition through the lens of risk, paying particular attention to the diagnostic method's reliability and improving prediction accuracy in the maintenance of industrial plant machinery.

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.

Construction of Smart Agricultural Precision Services in the Context of Agricultural and Rural Modernization

Smart agriculture relies on the Internet of Things, sensors and other technologies to intelligently manage agricultural production and improve the efficiency of production and operation. Smart agriculture relies on the automated control of the agricultural production process, automated early warning functions and application advantages to achieve the sustainable development goals of optimizing the use of resources, improving the quality of life. However, the current smart agriculture precision service still exists problems such as insufficient support, high cost and lack of talents. Focusing on the above problems, the article puts forward countermeasures and suggestions for the construction of smart agriculture precision service from the aspects of technical support, cost control, talent training, innovation and research and development.

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.