Production Process Control Research Articles

Teaching-Learning Strategies to Production Planning and Control Concepts: Application of Scenarios to Sequencing Production with Virtual Reality Support

This paper aims to present scenarios to be applied in higher education to the theme of production planning and control, addressing factors of the production system and indicators arising from this process and the application of virtual reality to support the process. The applied method combines the development of six scenarios for virtual reality application and the discussion about the impacts in indicators from the production planning and control, for example, inventory in the process, manufacturing lead-time, use of equipment, and punctual delivery attendance. Findings revealed that the teaching-learning process of production planning and control, when applied through scenarios, generates opportunities for students to learn the impact in the indicators. The virtual reality in this environment supports creating differentiated teaching-learning environments to generate the most significant knowledge for students which positively impacts the future in the world of work. In addition, it allows people involved in the teaching-learning processes of production engineering to apply the concepts presented in the sequencing process, lean about the impacts of decisions on production sequencing indicators and appreciate the support of virtual reality to generate an environment more cognitive for students.

Automated system for monitoring and control of the liquid wax production process

This article describes the design of an automated system for the automatic monitoring and control of the liquid wax production process, in order to quantify its effect on productivity indicators. For which initially the procedure for obtaining the automation will be described; then the results obtained will be presented, the same ones that will be identified through a comparative analysis. During the investigation it was determined that, through the use of a programmable logic controller, it was possible to improve the precision of the dosage of components in the liquid wax production process; By achieving acorrect dosage, it is achieved that the physical-chemical factors that intervene in the quality of the final product, which are the pH and specific density, are within the limits established by the company, this is reflected in the decrease 38.77% of the amount of monthly loss of raw material, thus achieving the optimization of the productivity of the production of liquid wax by 83.69% per month, compared to the non-automated process.<br /><div> </div>

Hydraulic pump automatic test system based on C/S structure

In order to meet the automation production and testing needs of modern enterprises, this paper takes the hydraulic vane pump test bench as an example, and builds a set of hydraulic automatic measurement and control system based on C/S framework, which can meet the requirements of single/double pump test simultaneously. Among them, the client software is developed based on LabVIEW, the server is built based on FTP protocol and MYSQL database, and the test bench is successfully connected to the internal production management system of the enterprise through the local area network, realizing the integration of product model management, process control, automatic testing, result uploading and other functions. At the same time, the PID closed-loop control strategy was improved in the acceleration and loading stages to shorten the test time and reduce the system overshoot. Finally, the automatic run-in, efficiency verification and low-speed pressure-holding test of vane pump have been completed.

Optimizing the Preventive Maintenance Scheduling Based on Dynamic Deterministic Demand in The Cement Manufacturing

Preventive maintenance is a planned and scheduled maintenance method that is carried out before a machine failure occurs. The maintenance schedule can be determined based on experience, historical data, or recommendations. Selecting the maintenance schedule greatly affects the production system. The Clin machine in cement manufacturing has an important role in the cement production process. During treatment, the client machine cannot produce clinker, so it is necessary to plan a production system to meet the demand. This paper aims to design an optimization model for determining the preventive maintenance schedule for cement manufacturing by considering the production process and inventory control. Mathematical models with binary options are used to model that system. The model supports showing the optimal preventive maintenance schedule for the ciln machines with a binary decision each period. This mathematical model describes the interaction of production planning, inventory control, and scheduling of total maintenance on a kiln machine. The goal of this system is to determine the optimal preventive maintenance schedule with minimum costs. In addition, the system's output is the optimal production and inventory decision rule for each period. Based on the analysis and simulation of the model with the deterministic and dynamic demand, the optimal preventive maintenance schedule is in the 9th and 21st periods. The kiln machines are maintained every July with minimal costs. The model scenario shows the interaction of the variables and the sensitivity of the production capacity and demand to the decision rule of the variable.

A control architecture for continuous production processes based on industry 4.0: water supply systems application

Industry 4.0 (I4.0) brings together new disruptive technologies, increasing future factories’ productivity. Indeed, the control of production processes is fast becoming a key driver for manufacturing operations. Manufacturing control systems have recently been developed for distributed or semi-heterarchical architectures, e.g., holonic systems improving global efficiency and manufacturing operations’ reactiveness. So far, previous studies and applications have not dealt with continuous production processes, such as applications for Water Supply System (WSS), oil refining, or electric power plants. The complexity of continuous production is that a single fault can degrade extensively and even cause service disruption. Therefore, this paper proposes the Holonic Production Unit (HPU) architecture as a solution to control continuous production processes. An HPU is created as a holon unit depicting resources in a continuous process. This unit can detect events within the environment, evaluate several courses of action, and change the parameters aligned to a mission. The proposed approach was tested using a simulated model of WSS. The experiments described in this paper were conducted using a traditional WSS, where the communication and decision-making features allow the application of HPU. The results suggest that constructing a holarchy with different holons can fulfill I4.0 requirements for continuous production processes.

CBI4.0: A cross-layer approach for big data gathering for active monitoring and maintenance in the manufacturing industry 4.0

Industry 4.0 (I4.0) defines a new paradigm to produce high-quality products at the low cost by reacting quickly and effectively to changing demands in the highly volatile global markets. In Industry 4.0, the adoption of Internet of Things (IoT)-enabled Wireless Sensors (WSs) in the manufacturing processes, such as equipment, machining, assembly, material handling, inspection, etc., generates a huge volume of data known as Industrial Big Data (IBD). However, the reliable and efficient gathering and transmission of this big data from the source sensors to the floor inspection system for the real-time monitoring of unexpected changes in the production and quality control processes is the biggest challenge for Industrial Wireless Sensor Networks (IWSNs). This is because of the harsh nature of the indoor industrial environment that causes high noise, signal fading, multipath effects, heat and electromagnetic interference, which reduces the transmission quality and trigger errors in the IWSNs. Therefore, this paper proposes a novel cross-layer data gathering approach called CBI4.0 for active monitoring and control of manufacturing processes in the Industry 4.0. The key aim of the proposed CBI4.0 scheme is to exploit the multi-channel and multi-radio architecture of the sensor network to guarantee quality of service (QoS) requirements, such as higher data rates, throughput, and low packet loss, corrupted packets, and latency by dynamically switching between different frequency bands in the Multichannel Wireless Sensor Networks (MWSNs). By performing several simulation experiments through EstiNet 9.0 simulator, the performance of the proposed CBI4.0 scheme is compared against existing studies in the automobile Industry 4.0. The experimental outcomes show that the proposed scheme outperforms existing schemes and is suitable for effective control and monitoring of various events in the automobile Industry 4.0.

Novel method for quantifying cells on carriers and its demonstration during SARS-2 vaccine development.

The most effective way to prevent and control infectious disease outbreak is through vaccines. The increasing use of vaccines has elevated the need to establish new manufacturing strategies. One of the major approaches is cell-based production, which creates a need for high cell density to enable higher cell production levels. This has led to development of the technology of cell carriers, including micro and macro cell carriers. To follow the production process, quantifying the number of cells on these carriers is required, as well as the tracking of their viability and proliferation. However, owing to various carriers' unique structures, tracking the cell's is challenging using current traditional assays that were originally developed for monolayers of adherent cells. The current "gold standard" method is counting cell nuclei, separating cells from the carrier, staining with crystal violet, and visually counting under a microscope. This method is tedious and counts both live and dead cells. A few other techniques were developed but were specific to the carrier type and involved specialized equipment. In this study, we describe a broadly ranging method for counting cells on carriers that was developed and employed as part of the development of severe acute respiratory syndrome coronavirus 2 vaccine. The method is based on the Alamar blue dye, a well-known, common marker for cell activity, and was found to be successful in tracking cell adsorption, cell growth, and viability on carriers. No separation of the cells from the carriers is needed, nor is any specialized equipment; the method is simple and rapid and provides comprehensive details necessary for process control of viral vaccine production in cells. This method can be easily implemented in any of a number of cell-based processes and other unique platforms for measuring the growth of encapsulated cells.

Reinforcement Learning for Statistical Process Control in Manufacturing

The main concept of the authors is to place Reinforcement Learning (RL) into various fields of manufacturing. As one of the first implementations, RL for Statistical Process Control (SPC) in production is introduced in the paper; it is a promising approach owing to its adaptability and the continuous ability to perform. The widely used Q-Table method was applied for get more stable, predictable, and easy to overview results. Therefore, quantization of the values of the time series to stripes inside the control chart was introduced. Detailed elements of the production environment simulation are described and its interaction with the reinforcement learning agent are detailed. Beyond the working concept for adapting RL into SPC in manufacturing, some novel RL extensions are also described, like the epsilon self-control of exploration–exploitation ratio, Reusing Window (RW) and the Measurement Window (MW). In the production related transformation, the main aim of the agent is to optimize the production cost while keeping the ratio of good products on a high level as well. Finally, industrial testing and validation is described that proved the applicability of the proposed concept.

Design of autonomous production using deep neural network for complex job

Deep Neural Network (DNN) in recent era offers new opportunities to manage the production systems with increasing complexity. In this paper, DNN is trained with discrete event simulation and a process based association to make the job shop an autonomous one. This intelligent system operates well in complex environment with constrained time limits while making optimal decisions. The DNN is henceforth combined with constrained time limits for the process of production control. The system is implemented typically in semiconductor manufacturing industries on complex job shops of a wafer fab case. The DNN is trained in such a way that it operates in complex environment with timing constraints that ships the job in accurate way without flaws in operation. The DNN rewards the selection with reduced time constraint, which tends to operates with most critical batch list. The study therefore shows that the DNN manages well the timing constraints than the standard benchmark technique.

Unveiling meloxicam monohydrate process of dehydration by an at-line vibrational multi-spectroscopy approach

Meloxicam (MLX) is a non-steroidal anti-inflammatory drug, extensively used for inflammatory diseases and pain treatments, which exhibits five known solids forms. Form IV of MLX, a zwitterionic monohydrate (MH), is an emblematic hydrate case with promissory dissolution properties in a poorly soluble drug. However, the lack of information about MH stability regarding the dehydration process and phase transition impedes the development of further stability studies.A multi-spectroscopic/chemometric approach was implemented coupling middle- (MIR), near-infrared (NIR) and Raman spectroscopies to monitor the heat-mediated dehydration process of MH. The application of multivariate curve resolution-alternating least squares (MCR-ALS) to multi-source spectra by data fusion allow a complete view of the phenomena, improving the selectivity and precision to establish the transition temperatures and to identify involved species.It was revealed a two-step mechanism, where MH changes to Form V at 90 °C obtaining its complete dehydration at 130 °C, Form V remains unchanged during the temperature range 130−190 °C and then the polymorphic conversion to Form I starts, which reaches 100 % at 230 °C before melting MLX (248 °C). The findings of this work allow set targets in the process control of products using MH. Additionally, MCR-ALS detected an event not evidenced by conventional thermal analysis, the transformation of Form V to Form I.

Protein L—More Than Just an Affinity Ligand

In the past 30 years, highly specific drugs, known as antibodies, have conquered the biopharmaceutical market. In addition to monoclonal antibodies (mAbs), antibody fragments are successfully applied. However, recombinant production faces challenges. Process analytical tools for monitoring and controlling production processes are scarce and time-intensive. In the downstream process (DSP), affinity ligands are established as the primary and most important step, while the application of other methods is challenging. The use of these affinity ligands as monitoring tools would enable a platform technology to monitor process steps in the USP and DSP. In this review, we highlight the current applications of affinity ligands (proteins A, G, and L) and discuss further applications as process analytical tools.

Cold rolling force model of nuclear power zirconium alloy based on Particle Swarm Optimization

The rolling force model is the core of rolling model, and the prediction accuracy of the Zr-4 alloy cold rolling force model directly affects the control accuracy of the thickness and shape of the Zr-4 alloy strip. In this paper, the cold rolling force model of the nuclear power Zr-4 alloy is studied based on the adaptive Particle Swarm Optimization (PSO) algorithm. The cold rolling deformation resistance model of the nuclear power Zr-4 alloy was obtained by field actual sampling and tensile test. The friction coefficient model of the nuclear power Zr-4 alloy was studied based on the Particle Swarm Optimization (PSO) algorithm according to the field measured rolling force value. According to the Hill model, a simplified method of external friction stress state coefficient model based on global variables is proposed by using the Particle Swarm Optimization (PSO) algorithm. Finally, the cold rolling force model of the Zr-4 alloy is established. Industrial test proves that the average cold rolling force prediction accuracy of the nuclear power Zr-4 alloy is 95.36%, which can effectively guide the rolling control of the nuclear power Zr-4 alloy cold rolling production process.

A Feasibility Study of SCADA Implementation at an Indonesian Oil Company

The implementation of Supervisory Control and Data Acquisition (SCADA) will make an activity more efficient because it will help control operation and production processes; control can be applied appropriately and at any time (real-time). This process will undoubtedly help the company to get maximum benefits. In 2020, the world oil price was very volatile and had touched a negative level. It is predicted that the uncertainty and low oil price will continue in the next few years. PT Pertamina Hulu Energi - Offshore South East Sumatra (PHE OSES), an Indonesian national company, plans to fully apply the SCADA system in its operational and production areas, because currently most of the PHE OSES areas still use the manual system. SCADA's estimated investment value of USD 8.6 million is a major investment for the company amidst the uncertainty of oil prices. Therefore, this project must get attention starting from the preparation, installation and implementation stages. In this article, economic analysis, cost & benefit analysis, and sensitivity test to factors that can affect economic value will be carried out. An analysis will also be carried out in terms of change management. All of these analyzes will answer whether the implementation of SCADA is feasible or not. The result of this research show that this project is feasible to be implemented in PHE OSES, it meets the capital budgeting criteria and has benefit to cost ratio larger than one. Oil price and oil losses recovery are the most affecting factors in the project’s feasibility. Company must do training, socialization, and hiring of experts play the vital role in the success of this project.

A Feasibility Study of SCADA Implementation at an Indonesian Oil Company

The implementation of Supervisory Control and Data Acquisition (SCADA) will make an activity more efficient because it will help control operation and production processes; control can be applied appropriately and at any time (real-time). This process will undoubtedly help the company to get maximum benefits. In 2020, the world oil price was very volatile and had touched a negative level. It is predicted that the uncertainty and low oil price will continue in the next few years. PT Pertamina Hulu Energi - Offshore South East Sumatra (PHE OSES), an Indonesian national company, plans to fully apply the SCADA system in its operational and production areas, because currently most of the PHE OSES areas still use the manual system. SCADA's estimated investment value of USD 8.6 million is a major investment for the company amidst the uncertainty of oil prices. Therefore, this project must get attention starting from the preparation, installation and implementation stages. In this article, economic analysis, cost & benefit analysis, and sensitivity test to factors that can affect economic value will be carried out. An analysis will also be carried out in terms of change management. All of these analyzes will answer whether the implementation of SCADA is feasible or not. The result of this research show that this project is feasible to be implemented in PHE OSES, it meets the capital budgeting criteria and has benefit to cost ratio larger than one. Oil price and oil losses recovery are the most affecting factors in the project’s feasibility. Company must do training, socialization, and hiring of experts play the vital role in the success of this project.

Induction coil as sensor for contactless, continuous in-process determination of steel microstructure by means of Magnetic Induction Spectroscopy (MIS)

In hot forming and machining process chains heat treatment of steels is controlled by temperature and time. The process parameters significantly influence the material microstructure, which is important for the final component properties. The dependence between the predominant microstructure and the magnetic properties can be determined by means of Magnetic Induction Spectroscopy (MIS). In this paper, we propose to use the induction coil as an in-process sensor element to implement MIS in induction heating processes with subsequent cooling of steel sheets. This novel approach enables contactless, continuous in-process measurement of complex inductance and allows a non-destructive determination of steel microstructure. The novel measurement method can differentiate discrete microstructural states and determine phase transformation points during continuous heating and cooling, by analysing the inductance curve. The realized combination of sensor and actuator features enables both process monitoring and closed-loop control of production processes with integrated heat treatment.

Research on multiphase flow measurement system based on electromagnetic correlation method

The phenomenon of oil-gas-water multiphase flow is common in oil Wells in the middle and late stage of oil field development. The accurate measurement of its flow is of great significance to the control of oil production process and the efficient development of oil fields. In the analysis and research on the existing problems of multiphase flow measurement technology at home and abroad and based on the proposed a flow measurement system based on conductance measurement technique design and the implementation plan, to achieve a kind of can expand production logging in multiphase flow of oil and gas flow measurement range, and with continuous measurement, no radioactive, low cost advantages, such as oil and gas water related method of flow measurement model of multiphase flow electromagnetic research, optimization of oilfield production and realization of intelligent well completion, and promote the development of new type multiphase flow instrument, with major production guiding significance and value.

The Industry 4.0 net information, measuring and control subsystems

The net information subsystems are for measuring processes application, universal and special cyber-physical production control processes through the calculation and link. The subsystems consist of calculation, technological and telecommunication devices united into open system architecture. The schemes are given of information, measuring and control subsystems, which coordination effort mechanisms are selectively to process a distributed information received through nets of electro and radio connection with a necessary passing through ability. The subsystem structure contains physical and virtual components, which include calculation, electronic and electromechanical elements united with general principles how to construct a communication nets for cyber-physical systems reception and transmission. The main attention is paid to the general data exchange processes as for measurements and control engaging the information vast data processing, which are formed in cyber-physical systems registered in different company local nets.

Features of the Digital Transformation of the Industrial Enterprise Management System in the Modern Conditions of Economic Development

The article examines the prerequisites for using the elements of digital transformation to improve the efficiency of industrial enterprises in the modern conditions of economic development. The authors identify and formulate the distinctive features of the transformation of the production process control system under the influence of digital transformation. The main provisions of the methodological substantiation of the transformation of the production and organizational structures of an industrial enterprise, involving the use of a system for allocating structural units into separate centers of financial and production responsibility, interacting with each other through “smart contracts” are revealed. The authors prove the relevance of the use of “smart contracts”, which allows self-regulation of production and management business processes of business entities. The stages of digital transformation of industrial enterprises are proposed, including: identification of the level of digital maturity of the enterprise; identification and development of the instrumental apparatus for the implementation of the digital transformation of the enterprise; assessment of the effectiveness of transformative actions aimed at the digital transformation of an economic entity. In the course of the proposed study, the conceptual foundations of the digital transformation of the enterprise management system are developed with the division into digital responsibility centers, and recommendations are formulated and proposed for the formation and development of the “digital field” of the enterprise.

Separation process control system in the cement production facility

The article developed a system for controlling the process of cement separation using neural networks. The control object is a separator. The task is to improve the cement production process control system by installing a frequency converter and an electric air damper. In order for the current frequency and the degree of opening of the damper to be set automatically, a neural network was developed for the separation process, which has two input and two output parameters. The neural network is able to adjust the rotor speed of the separator, relying on sensors that control the speed of the separator rotor and the size of the cement particles. If the speed of rotation of the rotor of the separator becomes low, artificial intelligence will adjust the fineness of grinding, changing the degree of opening of the damper for air supply to the separator. If the speed of rotation of the separator rotor approaches the maximum, and the grinding fineness has not reached the specified values, then the grinding fineness is controlled by changing the degree of opening of the damper for air supply to the separator.

Model of an automated information control system based on key performance indicators for controlling production processes

The article presents the problems of managing the key performance indicators of the organization’s production processes. Various situations of terminological conflict and other issues of interpretation of indicators reflecting the effectiveness of management of production systems are considered. A model of automated information and control system and its decomposition of components for managing the goals and objectives of structural divisions is proposed.