Process Control Strategies Research Articles

Experimentally validated predictive PI-PD control strategy for delay-dominant chemical processes

Controlling delay dominant chemical processes is a challenging control problem. Hence, a new method for designing a Smith predictor (SP)-based proportional-integral proportional-derivative (PI-PD) control strategy is proposed in this study. The inner-loop PD parameters are determined using the phase margin (PM) and maximum sensitivity (Ms) specifications. To determine the outer-loop PI parameters, the moment matching technique is augmented with maximum sensitivity considerations. The performance and robustness of this design is compared with that of its contemporaries using four benchmark chemical process models including that of a stirred tank reactor and a heat exchanger. The resilience of this control strategy amid uncertainties in process parameters is studied and performance improvement achieved over contemporary works is quantified. Finally, the proposed design is also experimentally validated on a two-tank level control loop.

Straightening rollformed profiles by controlled partial rolling

Roll forming is a sheet metal forming process in which flat sheets are incrementally formed into a desired profile geometry. The process is characterized by high material utilization and output. Along with these advantages, process-related longitudinal strains occur, causing profile defects such as bowing and twisting of the profile, which require subsequent straightening. Conventional straightening methods use overbending and twisting to eliminate existing profile defects. However, this approach is purely experience-based and iterative due to the complex interactions between the various bending and twisting operations. Therefore, a closed-loop control of the straightening process is currently not implemented. This can be remedied by the novel knowledge-based straightening method of partial rolling.In this paper, the basic idea of partial rolling will be introduced and the idea of knowledge-based process control will be demonstrated. A simulation model is then presented to test different process control strategies and evaluate their impact on the profile defects. Finally, an outlook is given on how a closed-loop controlled partial rolling process could be set up in a real process.

A decentralized control strategy for improved batch tablet coating process operation

This paper proposes a control strategy for batch tablet film-coating processes using readily available process instrumentation. Outlet air conditions (temperature and relative humidity), which impact coating quality, are controlled by the manipulation of inlet air temperature and coating spray rate. Controller design, tuning and validation rely on the use of a dynamic process simulator, calibrated using data collected from a pilot plant coating unit. The final 2 × 2 decentralized PI controller demonstrated good performance for both setpoint tracking and disturbance rejection in simulation. Additionally, controlled variable setpoints were determined to maximize plant profits considering batch duration and energy consumption as optimization criteria. For the case study considered, the total batch processing time can potentially be reduced by 40 % while simultaneously reducing energy usage by 30 %, in comparison with the more conservative fixed recipe approach typically adopted in production.

A two-stage fault diagnosis strategy for air handling units via a backpropagation multidimensional Taylor network fitter and a novel statistical process control

Faults in Heating, Ventilation, and Air Conditioning Air Handling Units can lead to increased energy consumption and failure to meet human comfort standards. Accurate and prompt fault diagnosis of Air Handling Units is therefore critical. Although widely-used deep learning methods have shown high precision in fault diagnosis, they present two main drawbacks: 1) deep learning approaches are often “black box” models lacking interpretability and rely on labeled data; 2) their complex structures entail substantial computational resources, which may not be conducive to timely fault diagnosis. To overcome these challenges, this paper introduces a method for diagnosing faults in air handling units that combines a novel statistical process control strategy, a backpropagation multidimensional Taylor network fitter, and a D-matrix approach. The backpropagation multidimensional Taylor network, using a polynomial network with only one hidden layer, approximates nonlinear functions. Compared to deep learning models, it maintains high accuracy with a simpler structure, enabling rapid fault diagnosis. Additionally, the novel statistical process control strategy employs the Markov property for fault detection, characterized by low false alarm rates, interpretability, robustness, minimal labeled data requirements, and suitability for real-time monitoring. Experimental results validate the effectiveness of our method in diagnosing faults in air handling units, achieving an average precision of 99.9%, surpassing several commonly used techniques.

Robot welding process planning and process parameter prediction of medium-thick plate based on three-line laser

In order to realize the intelligence and flexibility of V- groove robot welding of medium-thick plates, a multi-layer welding process control strategy for welding with a weaving robot is proposed based on three-wire structural light vision. For the optical localization of the welding start point, a “rough first, accurate later” guidance approach is proposed. A technique utilizing Gaussian-weighted plane fitting is employed to model the groove and extract information about the weld shape. The welding torch’s orientation planning is based on the angular bisector of the groove, and the GA-BP neural network is utilized to forecast welding parameters for various welding techniques. It is shown that the strategy in this paper can guide the robot to complete the welding job. The error between the guide start point and the actual welding start point does not exceed 2 mm, and the predicted welding parameter results align with the welding technology standards.

Integration of rapid bioburden testing into production quality management systems and process control.

The move to integrated continuous bioprocessing (ICB), while providing a means for process intensification, can put added strain on process analytics when conventional methods are used. For instance, traditional microbial methods provide minimal value to ICB processes given that the time required for data to become available is much longer than a typical full cycle of the manufacturing process. Although rapid microbial detection has been in discussion for over 30 years, it is still not routinely deployed in commercial biopharmaceutical manufacturing. One contributing factor is the ability to integrate this technology into a process control strategy and existing quality systems. An understanding of the capability of microbial detection technology available today can be leveraged to implement a control strategy for bioburden monitoring in real time for process intermediates. One key tenet of this proposed control strategy is the use of a "two-tiered approach" wherein a fast (but possibly less sensitive) test is used to monitor the process and trigger further action for a second, longer duration test which is used to confirm and quantify the presence of bioburden and identify the organism. This approach, presented here alongside several case studies for microbial monitoring, can have broader application for other process analytical technologies where fit for purpose methods could be employed to establish process control alongside real time continuous processes.

Solidification behavior and continuous preparation process of high-quality Zr-based metallic glass plates based on twin-roll strip casting

Metallic glasses exhibit excellent comprehensive properties in a variety of disciplines including mechanics, magnetism, electricity, and chemistry, making them highly promising for various applications. However, the current lack of continuous and efficient preparation technology for metallic glasses not only hinders the research and application of various metallic glass products but also contributes to the high cost associated with their production. In this study, we incorporate the twin-roll strip casting technology into the continuous fabrication process of metallic glass plates. By conducting numerical simulations and practical experiments, we elucidated the solidification process of metallic glasses in the twin-roll strip casting process. A process control strategy was developed focusing on "small molten pool height and low exit temperature", which enabled successful preparation of high-quality Zr55Cu30Al10Ni5 metallic glass plates ranging from 0.13 to 0.56 mm in thickness. A reduced pool height can enhance the cooling rate of metallic glasses and suppress the formation of vortices and crystals within the molten pool. By employing a lower exit temperature, it is possible to achieve a metallic glass plate characterized by high-energy microstructure, exceptional mechanical properties, as well as superior shape and surface quality. This research provides valuable guidance for continuous production of superior-grade metallic glass plates using twin-roll strip casting technique.

Feature similarity gradients detect alterations in the neonatal cortex associated with preterm birth.

The early life environment programmes cortical architecture and cognition across the life course. A measure of cortical organisation that integrates information from multimodal MRI and is unbound by arbitrary parcellations has proven elusive, which hampers efforts to uncover the perinatal origins of cortical health. Here, we use the Vogt-Bailey index to provide a fine-grained description of regional homogeneities and sharp variations in cortical microstructure based on feature gradients, and we investigate the impact of being born preterm on cortical development at term-equivalent age. Compared with term-born controls, preterm infants have a homogeneous microstructure in temporal and occipital lobes, and the medial parietal, cingulate, and frontal cortices, compared with term infants. These observations replicated across two independent datasets and were robust to differences that remain in the data after matching samples and alignment of processing and quality control strategies. We conclude that cortical microstructural architecture is altered in preterm infants in a spatially distributed rather than localised fashion.

Numerical Study of Thin-Walled Polymer Composite Part Quality When Manufactured Using Vacuum Infusion with Various External Pressure Controls.

The article presents the results of modeling various modes of vacuum infusion molding of thin-walled polymer-composite structures of arbitrary geometry. The small thickness of the manufactured structures and the fixation of their back surface on the rigid surface of the mold made it possible to significantly simplify the process model, which takes into account the propagation of a thermosetting resin with changing rheology in a compressible porous preform of complex 3D geometry, as well as changes in boundary conditions at the injection and vacuum ports during the post-infusion molding stage. In the four modes of vacuum-infusion molding studied at the post-infusion stage, the start time, duration and magnitude of additional pressure on the open surface of the preform and in its vacuum port, as well as the state of the injection gates, were controlled (open-closed). The target parameters of the processes were the magnitude and uniformity of the distribution of the fiber volume fraction, wall thickness, filling of the preform with resin and the duration of the process. A comparative analysis of the results obtained made it possible to identify the most promising process modes and determine ways to eliminate undesirable situations that worsen the quality of manufactured composite structures. The abilities of the developed simulation tool, demonstrated by its application to the molding process of a thin-walled aircraft structure, allow one to reasonably select a process control strategy to obtain the best achievable quality objectives.

Dynamic Modelling, Process Control, and Monitoring of Selected Biological and Advanced Oxidation Processes for Wastewater Treatment: A Review of Recent Developments.

This review emphasizes the significance of formulating control strategies for biological and advanced oxidation process (AOP)-based wastewater treatment systems. The aim is to guarantee that the effluent quality continuously aligns with environmental regulations while operating costs are minimized. It highlights the significance of understanding the dynamic behaviour of the process in developing effective control schemes. The most common process control strategies in wastewater treatment plants (WWTPs) are explained and listed. It is emphasized that the proper control scheme should be selected based on the process dynamic behaviour and control goal. This study further discusses the challenges associated with the control of wastewater treatment processes, including inadequacies in developed models, the limitations of most control strategies to the simulation stage, the imperative requirement for real-time data, and the financial and technical intricacies associated with implementing advanced controller hardware. It is discussed that the necessity of the availability of real-time data to achieve reliable control can be achieved by implementing proper, accurate hardware sensors in suitable locations of the process or by developing and implementing soft sensors. This study recommends further investigation on available actuators and the criteria for choosing the most appropriate one to achieve robust and reliable control in WWTPs, especially for biological and AOP-based treatment approaches.

Energy-Saving Control Strategy for Multisleep States of CNC Machine Tool Considering Components Priority

As the representative energy-consuming equipment in the manufacturing industry, machine tools consume lots of energy during the non-machining period, which leads to low energy efficiency. The existing strategies do not consider the startup sequence among components, which causes the control parameters to fail to run during the practical startup process, and this paper conducts energy-saving research to address the issues. The relationship of priority among components was used as the constraint to obtain an energy-saving control strategy for multi-sleep states of machine tools considering components priority. Furthermore, the improved whale algorithm was applied to solve the energy-saving control strategy of the machine tool, and the search and convergence ability of the algorithm was improved by introducing the chaotic convergence factor and adaptive weight coefficient. Finally, the energy-saving strategy was utilized with a vertical CNC machine tool as an experimental example to mill the end face of the randomly arriving workpieces. The results show that this changed algorithm has a better solution speed and effect than the ordinary whale algorithm and some other improved whale algorithms. Compared with the practical control process and other control strategies, the control strategy considering components priority can obtain more stable productivity and better energy-saving effect.

Key Process Parameters Study for the Fill Finish of Vaccines Containing Aluminum Hydroxide Adjuvant

Vaccine manufacturing is one of the most challenging and complex processes in pharmaceutical industry, and the process control strategy is critical for the safety, effectiveness, and consistency of a vaccine. The efficacy of aluminum salt adjuvant on vaccines strongly depends on its physicochemical properties, such as size, structure, surface charge, etc. However, stresses during the vaccine manufacturing may affect the stability of adjuvant. In this study, the impacts of cold/thermal stress, autoclaving, pumping, mixing, and filling shear stress on the physicochemical properties of aluminum hydroxide (AH) adjuvant were evaluated as part of the manufacturing process development. The results showed that the autoclaving process would slightly influence the structure and properties of the investigated AH adjuvant, but thermal incubation at 2–8 °C, 25 °C and 40 °C for 4 weeks did not. However, -20 °C freezing AH adjuvant led to the adjuvant agglomeration and rapid sedimentation. For the high shear stress study with mixing at 500 rpm in a 1-L mixing bag and pumping at 220 rpm for up to 24 h, the average particle dimension of the bulk AH adjuvant decreased, along with decreasing protein adsorption ratio. The studies indicate that various stresses during manufacturing process could affect the structure and physicochemical properties of AH adjuvant, which calls for more attention on the control of adjuvant process parameters during manufacturing.

Enhancing β-farnesene production in engineered Yarrowia lipolytica: A new process control strategy

β-Farnesene, a sesquiterpene with diverse applications, is applied in fields ranging from bioenergy and agriculture to medicine. In this study, a series of fermentation conditions and process control strategies were employed to enhance the accumulation of β-farnesene. Through the optimization of medium components, a suitable environment was created for β-farnesene biosynthesis in Yarrowia lipolytica, using only three inorganic salts as the initial medium. On this basis, cell growth and citrate supply were enhanced by adjusting the feeding time point, which begins when dissolved oxygen level below 40%. Additionally, initial cell density and glucose concentration during fermentation were separately optimized. Ultimately, nitrogen-limited conditions were applied to maximize carbon flux towards β-farnesene synthesis and the ideal condition was found to be C/N ratio of 100. Under the optimal condition, the titer of β-farnesene was increased by 227%, reaching 22.04 g/L in a 2 L bioreactor. This study optimizes terpenoid biosynthesis fermentation control and establishes a platform for the potential utilizing of waste biomass in Y. lipolytica.

Identification of a population balance model for Streptococcus thermophilus

The identification of a cell population balance model for the fermentation of the lactic acid bacterium Streptococcus thermophilus based on experimental data is addressed. As a starting point, a mass balance model is parameterized and then cell division rates identified exploring data from cell size distribution measurements as well as microscope images. The particular problem lies in the fact that Streptococcus th. grows in chains build up during cell division in dependence on physico-chemical parameters like lactic acid concentration, while cell size distribution (CSD) measurements do not clearly distinguish between cell and chain size. It is shown that, by using a factorized function for the cell division rate that reflects these different effects and can be identified using experimental data, it is possible to obtain an adequately parameterized model that is able to predict the time evolution of the CSD, opening the path for different process monitoring and control schemes.

Enhancing trust and security in IoT computing offloading through game theory and blockchain‐based control strategy

SummaryCollaboration between IoT end‐devices and edge servers or idle devices enables offloading of computational tasks, providing an effective solution to address inherent limitations in computational resources, storage capacity, and energy efficiency. However, IoT network openness introduces security challenges, such as privacy breaches, data security, and “free‐riding” attack. Securing computational offloading is crucial for service quality and reliability. In this article, we introduce a comprehensive approach to address these challenges. First, trust metrics are performed at the level of resource requester and resource provider respectively, and the trust level of both parties is matched to reduce the adverse effects of malicious devices or servers on computing offloading. Then, a distributed decision‐making method based on game theory is designed to improve the performance of the algorithm and avoid the single‐point‐of‐failure problem through the joint participation of multiple edge servers in decision‐making. Finally, a blockchain‐based task offloading process control strategy is used to deal with the “free‐riding” attack. The results of simulation experiments show the effectiveness of our proposed offloading decision‐making method.

Metabolic regulatory network kinetic modeling with multiple isotopic tracers for iPSCs.

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize the underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.

Event-Based Switching Iterative Learning Model Predictive Control for Batch Processes With Randomly Varying Trial Lengths.

Iterative learning model predictive control (ILMPC) has been recognized as an excellent batch process control strategy for progressively improving tracking performance along trials. However, as a typical learning-based control method, ILMPC generally requires the strict identity of trial lengths to implement 2-D receding horizon optimization. The randomly varying trial lengths extensively existing in practice can result in the insufficiency of learning prior information, and even the suspension of control update. Regarding this issue, this article embeds a novel prediction-based modification mechanism into ILMPC, to adjust the process data of each trial into the same length by compensating the data of absent running periods with the predictive sequences at the end point. Under this modification scheme, it is proved that the convergence of the classical ILMPC is guaranteed by an inequality condition relative with the probability distribution of trial lengths. Considering the practical batch process with complex nonlinearity, a 2-D neural-network predictive model with parameter adaptability along trials is established to generate highly matched compensation data for the prediction-based modification. To best utilize the real process information of multiple past trials while guaranteeing the learning priority of the latest trials, an event-based switching learning structure is proposed in ILMPC to determine different learning orders according to the probability event with respect to the trial length variation direction. The convergence of the nonlinear event-based switching ILMPC system is analyzed theoretically under two situations divided by the switching condition. The simulations on a numerical example and the injection molding process verify the superiority of the proposed control methods.

Loss-on-Drying Prediction for a Vibrated Fluidised Bed Dryer by Means of Mass and Energy Balances

PurposeContinuous wet granulation and drying require an adequate process control strategy to ensure the product quality. The most important critical quality attributes of dried granules are the granule size distribution and moisture content. Process analytical technologies (PATs) are available for real-time monitoring of moisture content by, e.g., near-infrared spectroscopy (NIRS), which requires additional installation and complex multivariate validation. Thus, a mass and energy balance (MEB) was derived for a vibrated fluidised bed dryer, which is part of the QbCon® 1 intended for continuous wet granulation and drying.MethodProcess parameters that are frequently logged were used for the derivation of a MEB. The predicted MEB was compared with the measured loss-on-drying (LOD) for two different formulations.ResultsThe model-derived data were in good agreement with the observed LOD, leading to RMSE values of 0.12–0.45.ConclusionThe implemented MEB can predict the LOD over time and thus might be suitable as a soft sensor without the installation of additional sensors. The obtained energy flux gives insight into the heat transfer, and the derived energy balance might be used to determine the required energy under certain drying conditions.

Concrete 4.0 ‐ Sustainable concrete construction with digital quality control

AbstractConcrete is a mass commodity with more than approx. 250 Mio. batches of concrete being produced every year in Europe alone. Despite this enormous repetition rate, both the quality inspection of the concrete raw materials as well as of the mixed concrete are still primarily batch‐based, relying on manual processes, thus hindering the application of modern process control schemes. The reasons for this lack are on the one hand to be seen in the lack of suitable sensor technologies and on the other hand in missing control algorithms. The paper at hand gives an overview on the current state of the art sensor technologies for both raw material as well as concrete properties measurement. Further, it outlines a pathway towards an online concrete process control.

Repetitive process based indirect-type iterative learning control for batch processes with model uncertainty and input delay

This paper develops an indirect iterative learning control scheme for batch processes with time-varying uncertainties, input delay, and disturbances. In this paper, a predictor based on a state observer is designed to estimate the future state and to compensate for the input delay. Then a feedback controller based on the estimated state and the set-point error is used to track the specified reference trajectory, where, of the options available, a robust H∞ controller is designed in the presence of time-varying uncertainties and load disturbances. Then a proportional plus derivative type iterative learning control law is designed. An injection molding process model demonstrates the new method’s effectiveness, and a comparison with a direct-type design is given.