Process Control Requirements Research Articles

Accelerated MPC: A real-time model predictive control acceleration method based on TSMixer and 2D block stochastic configuration network imitative controller

Modern industrial processes demand real-time model predictive control (MPC) method within the constraints of limited computing resources. This paper introduces an accelerated MPC method to address the issue. Specifically, we employ the time series mixing model (TSMixer) to construct a predictive model capable of accurately forecasting the behavior of the system under control. Furthermore, we extend the capabilities of TSMixer by integrating a feature classification model (TSMixer with FCM). This enhanced version takes into account both future information and static variables, resulting in superior accuracy compared to the transformer model while maintaining a significantly smaller parameter count. Finally, to further enhance MPC’s responsiveness, we develop a two-dimensional block stochastic configuration network (2D-BSCN) imitative controller. This innovative network clones the behavior of rolling optimization, effectively replacing online optimization to reduce computational time. The experimental results show that the accelerated MPC has an excellent performance in numerical simulation and actual industrial processes. Notably, the algorithm achieves a smaller FLOPs and tracking time than other state-of-the-art models, well within the requirement for industrial process control.

Improving carbon and nitrogen removal efficiency in high-strength nitrogen wastewater via two-stage nitritation-anammox process

Centralized systems employ biological treatments to remove nutrients, whereas decentralized systems, especially in rural areas, face challenges in implementing them. These systems are low flow but high carbon and nitrogen concentrations, and low C/N ratios. Source-separated wastewater (urine + flush) is an extreme example, like confined animal-feeding wastewater. High nutrient concentrations necessitate innovative technologies typically not employed for decentralized systems. This research demonstrates a simple two-stage nitritation-anammox system to treat high N concentration wastewater (pure urine + flush water). The systems consisted of a Membrane Aerated Biological Reactor (MABR) which performed carbon oxidation and nitritation and novel anammox reactors, (Pancopia AnammoX (PAXs)). The MABRs achieved OC removal over 108–198 g-C/m3-day with ∼ 97% removal, TAN oxidation rates up to 156 g-N/m3-day and nitrite/AN ratios were near 1 with no external control (e.g. pH, DO, cell wasting) other than urine loading rate. The PAXs achieved maximum AN removal efficiencies of 85–97%. The main limitation to complete TN removal in the PAXs was insufficient NO2- in the influent (nonideal influent NO2-/AN ratio). This system was not optimized for energy efficiency and had low volumetric conversion rates compared with other systems. However, it demonstrates that source-separated wastewater can be effectively treated with near complete N removal using a system with minimal process control requirements, lack of solid production, and elimination of diffuse aeration preventing odor generation. These attributes are attractive for applications where conditions do not allow for more complex high-rate systems such as developing societies and rural areas.

Model-based control system design to manage process parameters in mammalian cell culture for biopharmaceutical manufacturing.

To enhance the robustness and flexibility of biopharmaceutical manufacturing, a paradigm shift toward methods of continuous processing, such as perfusion, and fundamental technologies for high-throughput process development are being actively investigated. The continuous upstream process must establish an advanced control strategy to ensure a "State of Control" before operation. Specifically, feedforward and feedback control must address the complex fluctuations that occur during the culture process and maintain critical process parameters in appropriate states. However, control system design for industry-standard mammalian cell culture processes is still often performed in a laborious trial-and-error manner. This paper provides a novel control approach in which controller specifications to obtain desired control characteristics can be determined systematically by combining a culture model with control theory. In the proposed scheme, control conditions, such as PID parameters, can be specified mechanistically based on process understanding and control requirements without qualitative decision making or specific preliminary experiments. The effectiveness of the model-based control algorithm was verified by control simulations assuming perfusion Chinese hamster ovary culture. As a tool to assist in the development of control strategies, this study will reduce the high operational workload that is a serious problem in continuous culture and facilitate the digitalization of bioprocesses.

INTELLIGENT CONTROL SYSTEMS IN OIL REFINING PROCESS

The oil refining industry is an industry with high requirements for process control. Nowadays, an important problem is the creation of the concept of control systems for oil companies and enterprises. Oil is one of the Earth's most important resources. Its extraction and processing have become relevant for mankind. However, in the modern environment, the tasks of extracting oil with the latest equipment, as well as the use of cleaning and processing with minimal losses, have become important. This paper discusses intelligent control systems and their use in the oil refining industry. The purpose of the abstract is to create a structure that will be useful in the respective enterprises. But in order to achieve the goal of the work, it will be necessary to take into account such common problems as the instability of demand; restrictions imposed by the state and many others. That is why the features of each enterprise, which may bring various risks in the future, are considered at the design stage. Also, (it is necessary to indicate that,) the implementation of intelligent control systems in various industries is a time-consuming process. In these situations, the heads of enterprises need to take into account the human factor and help their employees adapt to new systems in every possible way. Among these methods, there are incentives with awards, the introduction of a team to train how to work with intelligent systems, and others. To delve into this topic, it is initially necessary to consider intelligent control systems separately from the oil refining industry. Keywords: automated control systems, deposits, oil refineries, quality of intelligent systems.

Development and Application of Field Mixing Process and Intelligent Mixing Vehicle for Plateau Type Emulsified Explosives for Open Pit Mining

Abstract To improve mine production efficiency, reduce blasting costs, and meet the needs of mine capacity expansion, the use of an on-site mixing emulsion explosives truck for open pit iron ore deep hole step blasting charging operation. This paper firstly briefly introduces the process flow of emulsion explosives production and makes a detailed introduction to the working principle and process control requirements of each section of emulsion explosives production. Based on the analysis of the basic principle of adaptive control, the adaptive PID controller is designed to control the discharge temperature and density of emulsion explosives production and to achieve self-tuning of parameters, which improves the control accuracy of production parameters and product quality. The research results show that the use of field mixing emulsion explosives, and blasting operations personnel was reduced by 40% to 50%, and the drilling workload was reduced by about 7%. The application of field mixing emulsion technology improves the blasting effect, increases shovel loading efficiency, reduces the labor intensity of workers, saves comprehensive mining costs, and reduces the cost of temporary explosives depot guarding and disposal of used packaging in mines.

Cloud-Edge-Terminal-Based Synchronized Decision-Making and Control System for Municipal Solid Waste Collection and Transportation

Due to dynamics caused by factors such as random collection and transportation requirements, vehicle failures, and traffic jams, it is difficult to implement regular waste collection and transportation schemes effectively. A challenge for the stable operation of the municipal solid waste collection and transportation (MSWCT) system is how to obtain the whole process data in real time, dynamically judge the process control requirements, and effectively promote the synchronization operation between multiple systems. Based on this situation, this study proposes a cloud-edge-terminal-based synchronization decision-making and control system for MSWCT. First, smart terminals and edge computing devices are deployed at key nodes of MSWCT for real-time collection and edge computing analysis of the whole process data. Second, we propose a collaborative analysis and distributed decision-making method based on the cloud-edge-terminal multi-level computing architecture. Finally, a “three-level and two-stage” synchronization decision-making mechanism for the MSWCT system is established, which enables the synchronization operation between various subsystems. With a real-world application case, the efficiency and effectiveness of the proposed decision-making and control system are evaluated based on real data of changes in fleet capacity and transportation costs.

Reliable nonlinear dynamic gray-box modeling by regularized training data estimation and sensitivity analysis

Dynamic process models are a key requirement for advanced process control and the application of process optimization techniques. The derivation of these models is time consuming and error-prone in cases where a lack of physico-chemical understanding is present. Machine learning (ML) methods can be employed in these cases to extract models or model elements from data. To reduce the amount of necessary data and to increase the extrapolation capabilities, gray-box models can be used that combine mechanistic equations with ML models. For embedded ML-models, the selection of a suitable model structure is challenging. Therefore, we propose a methodology to approach this problem in several steps by firstly estimating what values the ML-models should predict to accurately describe the experimental data. Subsequently, the ML-submodels can be trained using any ML-toolbox. Finally, a full parameter estimation is performed using a dynamic simulation in the cost function. We investigate different algorithmic options and show promising results for a case study of the fermentation of a sporulating bacterium.

Simultaneous Dimensional and Analytical Characterization of Ordered Nanostructures.

The spatial and compositional complexity of 3D structures employed in today's nanotechnologies has developed to a level at which the requirements for process development and control can no longer fully be met by existing metrology techniques. For instance, buried parts in stratified nanostructures, which are often crucial for device functionality, can only be probed in a destructive manner in few locations as many existing nondestructive techniques only probe the objects surfaces. Here, it is demonstrated that grazing exit X-ray fluorescence can simultaneously characterize an ensemble of regularly ordered nanostructures simultaneously with respect to their dimensional properties and their elemental composition. This technique is nondestructive and compatible to typically sized test fields, allowing the same array of structures to be studied by other techniques. For crucial parameters, the technique provides sub-nm discrimination capabilities and it does not require access-limited large-scale research facilities as it is compatible to laboratory-scaleinstrumentation.

Activity-Based Cell Sorting Reveals Resistance of Functionally Degenerate Nitrospira during a Press Disturbance in Nitrifying Activated Sludge.

ABSTRACTManaging and engineering activated sludge wastewater treatment microbiomes for low-energy nitrogen removal requires process control strategies to stop the oxidation of ammonium at nitrite. Our ability to out-select nitrite-oxidizing bacteria (NOB) from activated sludge is challenged by their metabolic and physiological diversity, warranting measurements of their in situ physiology and activity under selective growth pressures. Here, we examined the stability of nitrite oxidation in activated sludge during a press disturbance induced by treating a portion of return activated sludge with a sidestream flow containing free ammonia (FA) at 200 mg NH3-N/liter. The nitrite accumulation ratio peaked at 42% by day 40 in the experimental bioreactor with the press disturbance, while it did not increase in the control bioreactor. A subsequent decrease in nitrite accumulation within the experimental bioreactor coincided with shifts in dominant Nitrospira 16S rRNA amplicon sequence variants (ASVs). We applied bioorthogonal noncanonical amino acid tagging (BONCAT) coupled with fluorescence-activated cell sorting (FACS) to investigate changes in the translational activity of NOB populations throughout batch exposure to FA. BONCAT-FACS confirmed that the single Nitrospira ASV washed out of the experimental bioreactor had reduced translational activity following exposure to FA, whereas the two Nitrospira ASVs that emerged after process acclimation were not impacted by FA. Thus, the coexistence of functionally degenerate and physiologically resistant Nitrospira populations provided resilience to the nitrite-oxidizing function during the press disturbance. These results highlight how BONCAT-FACS can resolve ecological niche differentiation within activated sludge and inform strategies to engineer and control microbiome function.IMPORTANCE Nitrogen removal from activated sludge wastewater treatment systems is an energy-intensive process due to the large aeration requirement for nitrification. This energy footprint could be minimized with engineering control strategies that wash out nitrite-oxidizing bacteria (NOB) to limit oxygen demands. However, NOB populations can have a high degree of physiological diversity, and it is currently difficult to decipher the behavior of individual taxa during applied selective pressures. Here, we utilized a new substrate analog probing approach to measure the activity of NOB at the cellular translational level in the face of a press disturbance applied to the activated sludge process. Substrate analog probing corroborated the time series reactor sampling, showing that coexisting and functionally degenerate Nitrospira populations provided resilience to the nitrite oxidation process. Taken together, these results highlight how substrate analog approaches can illuminate in situ ecophysiologies within shared niches, and can inform strategies to improve microbiome engineering and management.

In-situ sensing, process monitoring and machine control in Laser Powder Bed Fusion: A review

Process monitoring and sensing is widely used across many industries for quality assurance, and for increasing machine uptime and reliability. Though still in the emergent stages, process monitoring is beginning to see strong adoption in the additive manufacturing community through the use of process sensors recording a wide range of optical, acoustic and thermal signals. The ability to acquire these signals in a holistic manner, coupled with intelligence-based machine control has the potential to make additive manufacturing a robust and competitive alternative to conventional fabrication techniques. This paper presents an overview of the state-of the art of in-situ process monitoring in laser powder bed fusion processes and highlights some current limitations and areas for advancement. Also presented is an overview of real-time process control requirements, which when combined with the emergent process monitoring tools, will eventually allow for in-depth process control of the powder bed fusion process, which is essential for wide-scale industrial credibility and adoption of this technology.

Smart Ultrasonic Welding – A Versatile Interconnection Technology for Power Electronics Packaging

Abstract Ultrasonic welding is a common and indispensable technology in the packaging of power electronics. Typical applications are terminals and power contacts, e.g. in motor drive or inverter modules, as well as busbar connections. The process control requirements for welding power electronics connectors or bus bars, e.g. on batteries, are increasingly overlapping with requirements typical for heavy wire bonding. Smart ultrasonic welding is a new technology combining the force and ultrasonic power of conventional ultrasonic welding equipment with the flexibility, precision, speed and advanced process control features of wire bonding machines. This contribution discusses the characteristics of smart welding equipment and presents process results for two different smart welding applications. On the low power end, smart welding and wire bonding are compared in the production of cylindrical cell battery packs, using a 100 W ultrasound system and aluminium connectors. Towards the high power end, 3 × 1.2 mm2 copper leads are welded to DBC with a contact area of 3 × 3 mm2, using a 1.5 kW ultrasound system. Both applications demonstrate advantages of smart welding over conventional ultrasonic welding.

Application of the Fog Computing Paradigm to Additive Manufacturing Process Monitoring and Control

Monitoring and controlling Additive Manufacturing (AM) processes play a critical role in enabling the production of quality parts. AM processes generate large volumes of structured and unstructured in-situ measurement data. The ability to analyze this volume and variety of data in real-time is necessary for effective closed-loop control and decision-making. Existing control architectures are unable to handle this level of data volume and speed. This paper investigates the functional and computational requirements for real-time closed-loop AM process control. The paper uses those requirements to propose a function architecture for AM process monitoring and control. That architecture leads to a fog-computing solution to address the big data and real-time control challenges.

Modeling and verification of running process control for underground mine locomotive

The traditional underground mine locomotive unmanned system is difficult to define requirements accurately and guarantee the functional safety and integrity. To solve this problem, a solution based on Event-B formal method is put forward. In this paper, we select the locomotive running process control for formal modeling and verification, which is the core of the underground mine locomotive unmanned system. Based on the Event-B formal method and Rodin platform, five refinements are proposed to model and verify the locomotive running process control with both discrete and continuous attributes. The correctness, consistency and deadlock-freeness of the system design in the actual situation are verified with extensive simulations. The verification and simulation results show that the solution meets the design requirements of the running process control of the underground mine locomotive.

METHODS FOR ENSURING LOW HUMIDITY IN THE INTERNAL VOLUME OF CERAMIC-METAL CASES IN SERIES PRODUCTION OF INTEGRATED CIRCUITS

Stricter requirements for reliability of integrated circuits in ceramic-metal packages led to the increased requirements for process and quality control of the atmosphere in which circuits are being sealed. In our experiments we manage to reduce humidity in integrated circuit packages, by the constant monitoring of the sealing process atmosphere.

Quality assurance strategy of pharmacy compounded drugs

Formulations and in-pharmacy preparation process of compounded drugs were being created for centuries, and seem to have no concerns regarding the quality, compatibility and stability during storage or usage. Taking in mind modern trends for quality assurance and standardization of drugs, quality of preparation process of compounded drugs can be proved by using of a systematic approach that is based on pharmaceutical quality systems. Variability of preparation conditions, preparation equipment from pharmacy to pharmacy, personal professional skills require the development of defined standards, rules and summarized the strategy of quality assurance of compounded drugs.
 The aim of this paper was determination of critical factors and development of the design for quality assurance of compounded drugs.
 The materials of this study were the regulative base of Ukraine, international documents, guidances and own investigations concerning the requirements for the preparation process and quality control of compounded drugs. The methods used were data systematical analysis, bibliosemantic and summary.
 The achievement of good quality of compounded drugs can be present as a step-by-step process, that cover adherence to necessary preparation procedure and knowledge of peculiarities of drugs, process understanding, identification of risks and its evaluation, minimization and implementation of the quality management system for documentation, equipment and personnel. As a result of preparation schemes analysis, the critical factors were determined and the Fishbone diagram was proposed. The factors contributing to the general strategy of compounded drugs quality assurance, such as prescription analysis, personnel, regulative base, conditions of preparation process and equipment, documentation, dispensing, characteristics of active ingredients and additives, compounding design, preparation technology, containers and package, quality control, stability and labelling were determined. The proposed diagram of causes and effects can be used as a template for the identification and quantification of risks during preparation and quality control of compounded drugs.
 The proposed general scheme for quality assurance is necessary for organization and optimization of monitoring preparation and quality control, implementation of remedial and preventive measures, tracking of causes, nonconformities, and prevention of its repetition, management of variables, determination of improvement possibilities and audit conducting.

Evaluation of in-line particle measurement with an SFT-probe as monitoring tool for process automation using a new time-based buffer approach

The Process Analytical Technology (PAT) initiative published by the Food and Drug administration (FDA) describes PAT systems as important tools to optimize pharmaceutical processes in order to ensure the final product quality. Real-time particle size measurements provide a promising approach for process automation of fluid bed granulation processes. This work focuses on the introduction and evaluation of a new time-based buffer approach for in-line spatial filtering technique (SFT) in order to provide a minimum time delay of the real-time measurement. Comparing the new buffer system to the conventionally used number-based approach, a reduced influence of the particle rate and a constant response time was found. A performance qualification of the Parsum probe IPP 80 using this buffer approach according to the ICH guideline Q2 was performed to investigate whether common variations of process parameters influence the real-time particle size measurement. Focussing on the main requirements for process control, hereby a good precision and specificity as well as an adequate robustness was found. Summarized, this study identified the time-based buffer system as more suitable buffer system for process automation.

Assessment of the predictive capabilities of commercial simulation software packages for the analysis of the C redistribution during Q&P processing

The C enrichment of austenite is of great importance to different kinds of transformation-induced plasticity-aided steels. Particularly, quenching and partitioning – a two-step heat treatment process, which involves the partial transformation to martensite and the subsequent C partitioning to austenite and its concomitant stabilization – has received considerable attention in recent years. The present work assesses the applicability of commercial software packages (DICTRA and MatCalc) to predict the interplay of C enrichment of austenite and concomitant C depletion of martensite. Results are additionally compared to calculations based on the “constrained carbon equilibrium” (CCE) and, moreover, to experiments conducted for two exemplary material conditions. DICTRA simulations extend the CCE calculation in terms of time dependence and allow to account for the possible displacement of the phase boundary, but lack the implications of the martensitic structure. Predictions based on MatCalc more accurately reflect experimental observations. Data gained allows for the evaluation of the predictive capabilities of commercial simulation packages corresponding to a prerequisite for the industrial implementation of novel heat treatment strategies and the required process control.

Strain localisation and failure of dissimilar magnesium-based alloy friction stir welds

Dissimilar butted combinations of magnesium alloys AZ31, AZ61 and AZ80 were friction stir welded and transverse tensile tested until failure. Strain mapping across the deforming weld nugget and interfaces was carried out to correlate the deformation to the local microstructures. Mechanical performance was found to be highly dependent on the spatial distribution of alloys within the nugget and thus highly sensitive to changes in the tooling geometry. Process control requirements for repeatable, high-quality dissimilar friction stir welds will therefore be far more rigorous and challenging than those of similar welds of these alloys. In addition, proximity to the tool insertion point was found to decrease tensile performance, likely due to changes in tooling temperature.

Process industries versus discrete processing: how system characteristics affect operator tasks

Despite an increasing level of automation, human operators still play a central role in industrial production. They need to monitor and adjust plant operations, compensate for process deviations, and step in when abnormal situations cannot be handled by the automation but require diagnosis and adaptive intervention. Based on a literature review, the article presents a cross-domain comparison of operator tasks and the associated knowledge and information requirements for process control in the process industries and discrete processing. While the process industries are characterized by a transformation of uniform, shapeless materials in physical or chemical processes, discrete processing is concerned with all subsequent steps in the mass production of consumer goods. It is argued that operator roles in these domains differ considerably. First, we compare technical system characteristics with regard to complex interactions, production processes, materials and products, faults and abnormal situations, and the information available to operators. Second, we describe how these technical system characteristics lay the foundation for similarities and differences in operator roles, focusing on qualification and training, routine task characteristics, dealing with abnormal situations, and the associated challenges for operators. We discuss implications for operator empowerment and operator support by assistance systems.

Auditing of explorative processes

Process management is a central part of quality management concepts like Total Quality Management, methods like Six Sigma and Lean but also of management systems standards such as ISO 9001, where it is represented by different requirements on process design, process control and process improvement. Research has been conducted on process management and the understanding of exploitation and exploration, but also the sometimes negative effects from process management practices on exploration in organisations when operating in competitive environments. The purpose of this paper is to study if internal auditors adapt to explorative processes when auditing ISO 9001 process management requirements. This paper is based on a qualitative case study in a global company in the consumer electronics industry. The study points towards that internal auditors apply ISO 9001 MSS requirements for process control across the studied organisations and processes even though the audited organisations and processes were considered to be explorative and the environment competitive. Not adapting requirements for process management, to an explorative process in a competitive environment, can stunt an organisation’s capability to be innovative, thereby negatively affecting its competitiveness.