Production Control System Research Articles

Sulfur dioxide in white sugar: Source of income, reference values

Sulfur dioxide is one of the classic essential technological processing aids used in modern technology for the production of white beet sugar. The technological and safe use of sulfites (sulfur dioxide, sodium sulfite and sodium hydrosulfite) in the formation of the food system of the sugar production process flow has been substantiated, and their functional effect is shown. It has been established that the change in the content of sulfur dioxide in white sugar under the influence of an antifoam, antiscale agent and decolorant in the doses used in massecuite occurred in accordance with the principle of additivity. The presence of the additive property was confirmed by the maximum approximation of the algebraic sum of the effects of each agent, amounting to 5.6%, to the range of variation in the values of the sulfur dioxide content (5.9) at a significance level of p=0.05. The dependence of the sulfur dioxide content in white sugar in a range of 0 to 6.5 mg/kg on the influence of the decolorant and defoamer in a dose range of 100 to 200 and of 4 to 6 g/t of massecuite was established. It has been revealed that increasing the minimum optimal dose of the decolorant to the maximum leads to an increase in the content of sulfur dioxide in white sugar by 1.7 times. Confidence intervals for the general mean were calculated based on the assumption of normal data distribution and a significance level of p=0.05. The reference values for the sulfur dioxide content in white sugar of the four categories were 1.28–2.69 mg/kg, and the upper limit level with consideration for the confidence interval was 1.39–3.01 mg/kg. The established reference values can be used at sugar factories in the production control system, and also provide an evidence base for comparison and assessment of this indicator of the white sugar safety for industrial consumers.

Self-learning and autonomously adapting manufacturing equipment for the circular factory

Abstract The integration of both linear and circular processes in one production system poses significant challenges. In particular, the reprocessing of end-of-life products is associated with uncertainties at all levels of the production system, from the initial planning and control through to the executing production hardware and intralogistics. To address these challenges, this article presents approaches for self-learning and autonomously adapting production equipment for the Circular Factory. Initially, hardware and software solutions are developed to cover the necessary processes. Reprocessing is covered by modular and reconfigurable manufacturing cells, which also include new process chains such as the combination of additive-subtractive processes. The provided capabilities must be applied to ever new products, for example by transferring human procedures for unknown products to the production equipment. Lastly, an overall robust and dynamic production planning and control system is developed that maintains continuous operation even in unforeseen situations. The resulting highly dynamic overall system is connected by an autonomous intralogistics system.

An innovative integrated framework for multi-level production planning and control systems in construction

The construction industry is plagued by chronic inefficiencies, such as low productivity, significant waste, cost overruns, and schedule delays. One of the most critical contributors to these issues is fragmented and ineffective planning and control methods. This research aims to address these challenges by developing an innovative integrated multi-level framework for production planning and control that harmonizes various planning methods and control metrics across different schedule levels. Employing the Design Science Research (DSR) methodology, this study systematically develops and evaluates an innovative framework that integrates internal and external project characteristics, utilizing the concept of "functionality" as a primary key for coherent integration. Furthermore, a multi-objective mathematical model is incorporated to clarify the framework's components and optimize the number of suggested planning methods and control metrics at each scheduling level, aiming to balance the functional requirements of project teams with the practicality of implementation.The application of the framework to a renovation case study demonstrates its practicality and strategic value, effectively addressing real-world project management challenges. Moreover, industry and academic evaluations highlight its potential to significantly improve construction project management practices.

Building information modeling-based production process optimization model

In the current manufacturing process of enterprises, there are some problems such as poor predictability and low level of intelligence, which lead to high product error rate and affect production efficiency. Therefore, this paper introduces the building information model in the field of engineering construction, and proposes a big data predictive manufacturing model based on the building information model, which divides the production process into production service system, resource planning system, production control system and after-sales service system, and realizes the overall process optimization of planning-production-sales on the basis of the close combination of each system and virtual model system. Finally, the application of error correction process in production line is verified from an empirical point of view, which provides a reference method and path for reducing production error rate and improving work efficiency.

The Effect of Pollutantmigration on the Environmental Situation in the Zone of Chemical Weaponinfluence

The article is devoted to the issues of monitoring the environment state in the influence zone of chemically hazardous objects on the example of the environmental monitoring system of a chemical weapons storage and destruction facility (CWDF) in the city of Kambarka, Udmurt Republic. Among the components there are: production control systems and site monitoring, environmental monitoring and health monitoring, however, it is environment in the zone of the chemical waste disposal facility influence which hasthe largest area and is the most difficult to control. Here, due to large areas, the process of migration of pollutants over considerable distances is observedclearly. The importance of the system for arranging sampling points on the topographic map of the waste disposal facility is shown in order to take into account the process of pollutantmigration. Vertical and horizontal migration of pollutants can lead to the emergence of unobvious local areas of pollutantaccumulation both within and outside the influence zone of the chemical waste disposal facility. A solution to the problem of migration process modeling on a conditionally selected area of the territory within the facility influence zone is presented to show the effect pollutantmigration on the dynamics of the environmental situation within the chemical waste disposal area. It has been proven that within the area under consideration there will be a significant amount of pollutant, which, subject to further evaporation of rainwater runoff, will remain in this area. This indicates the inevitability of the appearance of local areas of pollutantaccumulation in the influence zone of chemically-disposable substances in excess of permissible standards for their concentration, which requires a revision of the arranging sampling pointsystem, taking into account the application of migration processmodeling methods. The need for dynamic observations in individual sites of the area, selected with respect to the analysis results of the pollutantmigration routes within the entire influence zone of the chemically-disposable waste disposal facility, in order to assess the degree of impact of the non-toxic waste disposal facility on the environment, is substantiated.

Machine learning assisted prediction for hydrogen production of advanced photovoltaic technologies

The photovoltaic (PV) water electrolysis method currently stands as the most promising approach for green hydrogen production. The rapid iteration of photovoltaic technologies has significantly affected on the technical and economic evaluation for photovoltaic hydrogen production. In this work, the photovoltaic hydrogen production of three most advanced silicon photovoltaic technologies is systematically compared for the first time under the climatic conditions of the Kucha region. All-weather stable hydrogen production control system with optimal charging and discharging strategies is constructed to realize stable and efficient hydrogen energy production. Seven machine learning (ML) algorithms are used to forecast the performance in power generation and hydrogen production of a 100 ​MW photovoltaic hydrogen production and energy storage (PH-S) system throughout its operational life. The long short-term memory (LSTM) algorithm exhibits the best performance, achieving mean absolute error (MAE) of 0.0415, root mean square error (RMSE) of 0.0891, and coefficient of determination (R2) of 0.8402. In terms of cost-effectiveness, heterojunction with intrinsic thin layer (HJT) PV technology achieves the lowest levelized cost of electricity (LCOE) and hydrogen (LCOH) at 0.025 $/kWh and 6.95 $/kg, respectively. According to the sensitivity analysis, when the cost of proton exchange membrane electrolysis (PEMEC) reduced 50%, the LCOH for PH-S system decreased 21.40%. This study provides valuable insights for the practical implementation of large-scale photovoltaic hydrogen production and cost reduction in PH-S systems.

Digital Twin Modeling for Smart Injection Molding

In traditional injection molding, each level of the process has its own monitoring and improvement initiatives. But in the upcoming industrial revolution, it is important to establish connections and communication among all stages, as changes in one stage might have an impact on others. To address this issue, digital twins (DTs) are introduced as virtual models that replicate the entire injection molding process. This paper focuses on the data and technology needed to build a DT model for injection molding. Each stage can have its own DT, which are integrated into a comprehensive model of the process. DTs enable the smart automation of production processes and data collection, reducing manual efforts in supervising and controlling production systems. However, implementing DTs is challenging and requires effort for conception and integration with the represented systems. To mitigate this, the current work presents a model for systematic knowledge-based engineering for the DTs of injection molding. This model includes fault detection systems, 3D printing, and system integration to automate development activities. Based on knowledge engineering, data analysis, and data mapping, the proposed DT model allows fault detection, prognostic maintenance, and predictive manufacturing.

Implementasi Sistem Hidrolik Pada Mesin Spandek Produksi di PT Primaland

The purpose of this journal article is to provide research findings regarding the application of automated production control system theory to the construction industry. from expediting work processes. The method used in this research is to observe the production process of a Primalland light steel construction company in real time. Please note, raw materials can be obtained from literature studies, with a focus on their implementation in industrial processes. There are many challenges in the development sector in Indonesia, both in terms of quality and quantity, PT. PrimaLand Light Steel Frame was founded on December 25 2008 in Pandeglang. Based on the research that has been carried out, the data obtained from this research is then analyzed in detail to determine whether there is a significant difference between price changes that affect sales volume and price changes that affect sales volume. Research on sales volume at PT PrimaLand Pandeglang between 2014 - 2016. PrimaLand light steel tries as best as possible to maintain the quality of the roof truss profile produced by using high quality steel coils, or what can be called High Tension, namely with a yield strength range of Mega Pascal 400 Mpa – 500 Mpa and generally standard G550, or reaching 2x the strength from conventional melted steel which is more than 240 Mpa.

Development and Validation of Matrix of Chemistry, Manufacturing, and Control (MoCMC) System for Intramammary Drug Products (IMM).

Products formulated for intramammary (IMM) infusion are intended for the delivery of therapeutic moieties directly into the udder through the teat canal to maximize drug exposure at the targeted clinical site, the mammary gland, with little to no systemic drug exposure. Currently, to our knowledge, there has been no in-vitro matrix system available to differentiate between IMM formulations. Our goal is to develop A custom tailored in-vitro "Matrix of Chemistry, Manufacturing and Control" (MoCMC) System to be a promising future tool for identifying inequivalent IMM formulations. MoCMC can detect inter and intra batch variabilities, thereby identifying potential generics versus brand product similarities or differences with a single numeric value and a specific& distinctive fingerprint. The FDA-approved IMM formulation, SPECTRAMASTⓇ LC, was selected as the reference product for the MoCMC. Twelve in-house test formulations containing ceftiofur hydrochloride were formulated and characterized. The MoCMC was developed to include six input parameters and three output parameters. The MoCMC system was used to evaluate and compare SPECTRAMASTⓇ LC with its in-house formulations. Based on the MoCMC generated parameters, the distinctive fingerprints of MoCMC for each IMM formulations, and the statistical analyses of MCI and PPI values, in-house formulations, F-01 and F-02 showed consistency while the rest of in-house formulations (F-03-F-12) were significantly different as compared to SPECTRAMASTⓇ LC. This research showed that the MoCMC approach can be used as a tool for intra batch variabilities, generics versus brand products comparisons, post-approval formulations changes, manufacturing changes, and formulation variabilities.

Advancing Manufacturing Efficiency through Real-time Production Monitoring and Control Systems

The advantages of both real-time production monitoring and control systems in enhancing the advancement of manufacturing efficiency cannot be overlooked. Manufacturing industries need to meet customers’ demand while still maximizing their profits via avoiding emergency shutdown of plants during production. Nonetheless, quality products with high purity level should be supplied uninterruptedly without breakage of supply chain. In recent times, failure of many of manufacturing industries to deliver their products to end users have been cited and reported. This was due to inadequate scheduling which was not being monitored via real-time production and control systems. There is need to tackle this problem which calls for the adoption of real-time production monitoring and control systems. In this paper, the concepts behind the use of real-time production monitoring and control systems were discussed. Consideration was given to how real-time production monitoring can be integrated in manufacturing. The features of real-time control systems were discussed alongside with their industrial applications in various disciplines of Engineering. Data analysis and prognostics, data collection, visualization module and data storage were identified as the relevant sequential steps in real time production monitoring in advancing manufacturing efficiency. In conclusion, the significance and importance of real-time production monitoring and control systems in advancing manufacturing efficiency have been revealed and discussed in this paper.

Moving toward lean construction through automation of planning and control in last planner system: A systematic literature review

The Last Planner System (LPS) is a production planning and control system based on Lean Construction (LC) principles. Effectively implementing the LPS in the construction industry remains challenging, impeding the realization of LC principles. Automation of planning and control in the LPS has gained attention to address this challenge. However, research exploring the state-of-the-art of such automation and its impacts on realizing LC principles is still lacking. Therefore, this study performed a systematic literature review of 112 relevant studies, identifying 50 functionalities and associated benefits of automation across six LPS stages, with the lookahead planning stage having the maximum functionalities. Analyzing their interactions with LC principles revealed that automated planning and control in the LPS can support achieving ten LC principles. Future research directions are suggested in areas such as constraint management, knowledge reuse, dynamic replanning, and construction workforce training.

A self-learning framework combining association rules and mathematical models to solve production scheduling programs

ABSTRACT Data-driven production scheduling and control systems are essential for manufacturing organisations to quickly adjust to the demand for a wide range of bespoke products, often within short lead times. This paper presents a self-learning framework that combines association rules and optimization techniques to create data-driven production scheduling. A new approach to predicting interruptions in the production process through association rules was implemented, using a mathematical model to sequence production activities in real or near real-time. The framework was tested in a case study of a ceramics manufacturer, updating confidence values by comparing planned values to actual values recorded during production control. It also sets a production corrective factor based on confidence value and success rate to avoid product shortages. The results were generated in just 1.25 seconds, resulting in a makespan reduction of 9% and 6% compared to two heuristics based on First-In-First-Out and Short Processing Time strategies.

Combinatorial reasoning-based abnormal sensor recognition method for subsea production control system

The subsea production system is a vital equipment for offshore oil and gas production. The control system is one of the most important parts of it. Collecting and processing the signals of subsea sensors is the only way to judge whether the subsea production control system is normal. However, subsea sensors degrade rapidly due to harsh working environments and long service time. This leads to frequent false alarm incidents. A combinatorial reasoning-based abnormal sensor recognition method for subsea production control system is proposed. A combinatorial algorithm is proposed to group sensors. The long short-term memory network (LSTM) is used to establish a single inference model. A counting-based judging method is proposed to identify abnormal sensors. Field data from an offshore platform in the South China Sea is used to demonstrate the effect of the proposed method. The results show that the proposed method can identify the abnormal sensors effectively.

ПРАКТИЧЕСКИЙ ОПЫТ ПРИМЕНЕНИЯ ИНТЕРАКТИВНЫХ ЗАНЯТИЙ В МАГИСТРАТУРЕ «ПРОМЫШЛЕННАЯ ФАРМАЦИЯ»

Education of Master's degree students at the Institute of Pharmacy is carried out under the educational program "Industrial Pharmacy". Training is oriented to practical activity and forms professional competencies. The purpose of training in the Master's program is to learn to control the requirements for the production of medicines in pharmaceutical production, to implement the rules of the pharmaceutical quality system of production and quality control of medicines, to improve the pharmaceutical quality system of production of medicines. The set goals in training are achievable with the introduction of interactive methods in the classroom. Master's degree teacher is obliged not only to be competent in the issues of the discipline he teaches, but also must improve the learning process according to the modern requirement of the time. Interactive learning is characterized by joint activity of students and teacher. One of the interactive teaching methods is a round table. At the round table class the student consolidates the previously acquired knowledge, expands it by supplementing the missing information, learns to solve problems in the course of discussion. For this purpose, the teacher creates comfortable conditions when all students are involved in the learning process, they understand the material of the topic, know it and think about solving the problem posed by the teacher in the class. The study analyzed the experience of conducting an interactive lesson in the form of a round table in the discipline of pharmaceutical chemistry and drug analysis. The authors conducted a questionnaire survey of master's degree students. According to the results of the survey it was found out that the use of interactive methods in the educational process increased the effectiveness of learning for the majority of students. Almost all master's students participated in the discussion at the class, even those who previously behaved passively in learning. The majority of students noted that their uncertainty in front of the audience disappeared, communication with the teacher and other students during the round table facilitated rapid and productive assimilation of the material. The results of the study can be used in the professional activities of university teachers.

Application of Improved PSO and BP Hybrid Optimization Algorithm in Electrical Automation Intelligent Control

A fuzzy RBF-PID control strategy based on particle swarm optimization (PSO) algorithm is proposed to solve the problem of large inertia lag in temperature control system of industrial production refuse furnace. In this control system, an improved particle swarm optimization algorithm combined with inertia weight and genetic transformation was used to optimize the initial values of membership functions of fuzzy RBF (radial basis function). Then, BP (error backpropagation) algorithm is used for fine tuning, and fuzzy reasoning and RBF learning ability are combined to adjust the PID control parameters online to achieve the optimal PID control effect. The simulation results show that the algorithm has fast tracking, small overshoot, and is not easily trapped in local minima. At the same time, its robustness and anti-interference performance are better than traditional PID control.

Material flow control in make-to-stock production systems: an assessment of order generation, order release and production authorization by simulation

AbstractMaterial flow control (MFC) is a key element of production planning and control. The literature typically categorizes different MFC methods according to how MFC is realized. This distinction overlooks that MFC decisions can be subdivided into three independent tasks that are executed as orders progress through the system: (i) order generation, (ii) order release, and (iii) production authorization. MFC methods are typically designed for only one of these three tasks, which leaves a large part of the order flow uncontrolled. This study therefore not only provides a new categorization of MFC methods, but also argues for the simultaneous application (or the combining) of three different MFC methods for order generation, order release, and production authorization. To support this argument, the performance effects of an integrated MFC approach are evaluated. Findings show that each individual MFC method impacts different performance metrics, which can be explained by the presence of a hierarchy of workloads, where each workload level constrains the succeeding hierarchical level. Each MFC method has a main impact on a different workload. This has important implications for the design of MFC methods and extends recent literature on hierarchical production planning and control systems.

EFFECTIVE MATERIAL MANAGEMENT AND FIRM’S PRODUCTIVITY IN SELECTED ALUMINUM MANUFACTURING FIRMS IN DELTA STATE, NIGERIA

This study investigated how effective material management (MMGT) affects firm’s productivity in selected aluminum manufacturing firms in Delta State, Nigeria. The specific objectives of the study are to ascertain effect of the measures of effective MMGT, namely; Planning Strategy (PS), Material Control Management (MCM) and Work-In-Progress Management (WIPM) on Firm’s Productivity (FP). The respondents' responses were collected using a five (5) likert scale questionnaire. This study's population is 293 people, with a sample size of 169 employees from the selected Aluminium company in Delta State, Nigeria. The questionnaire was coded using an excel spreadsheet, and the respondents' profiles were analysed manually using basic percentage, while descriptive statistics, correlation matrix, and multiple regression used statistical tools in SPSS version 23. The findings showed that there is a substantial positive association between PS, MCM, and WIPM and FP. The study's findings indicate that good MMGT has a positive and significant effect on company productivity in selected Aluminium manufacturing enterprises in Delta State, Nigeria. As a result, the study proposed that Aluminium manufacturing firms adopt a policy framework to promote speedier implementation of material control systems in Nigerian Aluminium production in order to excel and ensure its future, hence increasing organisational efficiency.
 Keywords: Material, Management, Productivity, Planning, Strategy and Work-In-Progress.

The Influence Of The Production Control System And Marketing Mix On Post Covid 19 Income Levels In UMKM Selling Wet Cakes In Kampung Melayu Village, Banyuwangi District

This study aims to determine the effect of the production control system and marketing mix on income levels during the pandemic at the UMK Sales of Wet Cake Products in Kampung Melayu Village, Banyuwangi District. In this study, researchers used primary data. Primary data in this study were obtained by distributing questionnaires. This study used 5 sellers of wet cake products as samples. The analytical techniques used include instrument testing, classical assumption test, multiple linear regression analysis, coefficient of determination and hypothesis testing. The results showed that (1) the production control system partially significantly affected the level of income, (2) the marketing mix partially significantly affected the level of income, (3) and simultaneously the production control system and marketing mix had an effect on the level of income.

Hybrid multi-chamber system for enhanced removal of boron during water treatment/desalination without chemical usage

The efficiency of boron removal during water treatment and desalination is insufficient to meet the water quality standard set by the World Health Organization. Boron can exist in its two natural forms in water, depending on the water pH. At low and high pH, boron tends to exist as boric acid and borate ion, which are neutral and negatively charged, respectively. In this study, we developed a multi-chamber boron control system. For the control group (nanofiltration (NF) and reverse osmosis (RO)), the pH was controlled to enhance the boron removal efficiency. As expected, the boron removal efficiency at pH 11 (RO: 95–98%, NF: 91%) was higher than that at pH 8 (RO: 85–87%, NF: 80%). Additionally, activated carbon was introduced in the developed system (forward osmosis-NF/RO hybrid processes within the draw-solution chamber). Powdered activated carbon was more effective than granulated activated carbon in adsorbing boron molecules, with the maximum adsorption capacities (qm) of the former and latter being 2.33 and 1.33 mg g−1, respectively. A simple economic analysis was performed to determine the applicability of the multi-chamber boron control system for the economical production of high-quality water with minimal boron concentration. The unit cost of the multi-chamber boron control system (0.26 USD ton−1) was significantly lower than that associated with single NF and RO processes (0.64–1.62 USD ton−1). The multi-chamber boron control system thus represents an economical alternative that can achieve a higher product water quality compared with existing processes.

MODERN PRACTICE OF USING NON-DESTRUCTIVE TESTING METHODS IN TIRE PRODUCTION

Improving the competitiveness of Russian tires requires improving the organization of the quality control system in production. The purpose of the article is to analyze the current state of the application of tire quality control methods in production. A common way to diagnose tires in the Russian industry is random control by road tests and bench tests, leading to the destruction of the product. At the same time, non-destructive testing is widely used in the global tire industry. Tire structural strength, material homogeneity, stiffness and geometric characteristics of tires are checked on test benches, which are the result of defects that can be diagnosed in a “non-invasive” way, i.e. non-destructive testing methods. Visual-optical, eddy current, ultrasonic and radiation methods of non-destructive testing are the most optimal for diagnosing continuity and obtaining the physical and mechanical characteristics of rubber and cord. Continuous flaw detection control of tires allows for the lowest cost diagnostics of hidden defects and timely improvement of the technological process. The interferometric method of non-destructive testing can be considered as an intelligent predictive diagnostics of hidden defects, bubbles, delamination, foreign inclusions in tires with nanometer accuracy. Patents and practical developments of Russian inventors in the field of interferometry testify to the high potential of mass production of domestic flaw detectors for their use in tire factories.