Advanced Process Control System Research Articles

AI and the transformation of industrial work: Hybrid intelligence vs double-black box effect

It is uncertain how the application of artificial intelligence (AI) technology transforms industrial work. We address this question from the perspective of cognitive systems, which, in this case, includes considerations of AI and process transparency, resilience, division of labor, and worker skills. We draw from a case study on glass tempering that includes a machine-vision-based quality control system and an advanced automation process control system. Based on task analysis and background literature, we develop the concept of hybrid intelligence that implies balanced AI transparency that supports upskilling and resilience. So-called fragmented intelligence, in turn, may result from the combination of the complexity of advanced automation along with the complexity of the process physics that places critical emphasis on expert knowledge. This combination can result in the so-called “double black box effect”, given that designing for understandability for the line workers might not be feasible: expert networks are needed for resilience.

Advanced Process Control System for Trench Shape of Power Devices

In the semiconductor manufacturing, the manufacturing equipment is managed via the quality control (QC) in which the shape of the processed feature is checked whether it meets the specification. If the shape is out of the specification, some recipe parameters are modified so that the shape meets the specification. The calculation method of the recipe parameters depends on the know-how of the individual fab engineers, which cause difficulties in the QC. To overcome this problem, we have developed an automatic calculation method of the optimal recipe parameters with Advanced Process Control (APC) system with the stacking model method in order to solve these problems. In the development process, we have also performed comparison of two gap scoring methods. In the on-site demonstration of this system, we have further optimized the exploration method of the recipe parameters.

Machine Learning-Based Prediction of Controlled Variables of APC Systems Using Time-Series Data in the Petrochemical Industry

For decades, the chemical industry has been facing challenges including energy conservation, environmental protection, quality improvement, and increasing production efficiency. To address these problems, various methods are being studied, such as research on fault diagnosis for the efficient use of facilities and medium-term forecasting with small data, where many systems are being applied to improve production efficiency. The problem considered in this study is the problem of predicting time-series Controlled Variables (CV) with machine learning, which is necessary to utilize an Advanced Process Control (APC) system in a petrochemical plant. In an APC system, the most important aspect is the prediction of the controlled variables and how the predicted values of the controlled variables should be modified to be in the user’s desired range. In this study, we focused on predicting the controlled variables. Specifically, we utilized various machine learning techniques to predict future controlled variables based on past controlled variables, Manipulated Variables (MV), and Disturbance Variables (DV). By using a time delay as a parameter and adjusting its value, you can analyze the relationship between past and future data and improve forecasting performance. Currently, the APC system is controlled through mathematical modeling and research, The time-series data of controlled variables, manipulated variables, and disturbance variables are predicted through machine learning models to compare performance and measure accuracy. It is becoming important to change from mathematical prediction models to data-based machine learning predictions. The R-Squared (R2) and Mean Absolute Percentage Error (MAPE) metric results of this study demonstrate the feasibility of introducing an APC system using machine learning models in petrochemical plants.

Synergic Combination of Hardware and Software Innovations for Energy Efficiency and Process Control Improvement: A Steel Industry Application

The present paper proposes a steel industry case study focused on a reheating furnace and a rolling mill. Hardware and software innovations were successfully combined in order to obtain process control and energy efficiency improvement. The reheating furnace at study is pusher type and processes billets. The hardware innovation is related to the installation of an insulated tunnel at the end of the reheating furnace, in order to guarantee a higher heat retention of the billets before their path along the rolling mill stands. The software innovation refers to the design and the installation of an Advanced Process Control system which manipulates the gas flow rate and the stoichiometric ratio of the furnace zones in order to satisfy the control specifications on billets and furnace variables. The control system is based on Model Predictive Control strategy and on a virtual sensor which tracks and estimates the billet features inside/outside the furnace. The designed controller was commissioned on the real plant, providing significant performances in terms of service factor, process control, and energy efficiency.

Multi-Mode Model Predictive Control Approach for Steel Billets Reheating Furnaces.

In this paper, a unified level 2 Advanced Process Control system for steel billets reheating furnaces is proposed. The system is capable of managing all process conditions that can occur in different types of furnaces, e.g., walking beam and pusher type. A multi-mode Model Predictive Control approach is proposed together with a virtual sensor and a control mode selector. The virtual sensor provides billet tracking, together with updated process and billet information; the control mode selector module defines online the best control mode to be applied. The control mode selector uses a tailored activation matrix and, in each control mode, a different subset of controlled variables and specifications are considered. All furnace conditions (production, planned/unplanned shutdowns/downtimes, and restarts) are managed and optimized. The reliability of the proposed approach is proven by the different installations in various European steel industries. Significant energy efficiency and process control results were obtained after the commissioning of the designed system on the real plants, replacing operators' manual conduction and/or previous level 2 systems control.

Advances in bioreactor control for production of biotherapeutic products.

Advanced control strategies are well established in chemical, pharmaceutical, and food processing industries. Over the past decade, the application of these strategies is being explored for control of bioreactors for manufacturing of biotherapeutics. Most of the industrial bioreactor control strategies apply classical control techniques, with the control system designed for the facility at hand. However, with the recent progress in sensors, machinery, and industrial internet of things, and advancements in deeper understanding of the biological processes, coupled with the requirement of flexible production, the need to develop a robust and advanced process control system that can ease process intensification has emerged. This has further fuelled the development of advanced monitoring approaches, modeling techniques, process analytical technologies, and soft sensors. It is seen that proper application of these concepts can significantly improve bioreactor process performance, productivity, and reproducibility. This review is on the recent advancements in bioreactor control and its related aspects along with the associated challenges. This study also offers an insight into the future prospects for development of control strategies that can be designed for industrial-scale production of biotherapeutic products.

Advanced process control system with MPC as a new approach for layer of protection analysis

Layer of protection analysis (LOPA) is a widely used method to support process safety in the chemical industries. In the LOPA, the process is classified into many layers, one of such layers considers the basic process control system (BPCS) which commonly uses PID controllers. This kind of controllers cannot deal with constraints. For this reason, the main purpose of this work is to provide a framework to enhance the control layer in the LOPA, which consists of a model predictive control (MPC) with safety features. These features include: sublayers in the controller system (such as real time optimization, target calculation, and MPC), safety constraints, and guarantee of stability by adopting an Infinite Horizon MPC (IHMPC). Here, we propose an approach for control-inspired view to process safety, replacing the BPCS by an Advanced Process Control System (APCS). Moving forward with these concepts, first, a literature review emphasizes the content, showing two perspectives for the APCS. The APCS is designed for two varieties of controllers, a basic IHMPC and IHMPC with zone control to compare the performance. In this framework, the first sublayer consists of a real time optimization (RTO) structure, that calculates the optimal operating condition for the process controller, which computes the control action. Besides, RTO has an additional constraint called the safety index, based on the protection of process operational. RTO and basic IHMPC communicate directly, while for IHMPC with zone control there is an inner sublayer called Target Calculation, it computes a feasible target to the controller, working as another safety strategy in APCS. After that, we demonstrate both structures applied to a CSTR reactor. From the case study, we compared both controllers, and evaluated the effect that the safety index constraint causes in the setpoints, outputs, and control actions. The use of safety constraint in RTO proved to be a safe strategy for the control layer, as well as IHMPC with zone control presented a safer profile than basic IHMPC. Furthermore, the results show that safety constraint affect the economic goal, decreasing its value.

Reservoir Advanced Process Control for Hydroelectric Power Production

The present work is in the framework of water resource control and optimization. Specifically, an advanced process control system was designed and implemented in a hydroelectric power plant for water management. Two reservoirs (connected through a regulation gate) and a set of turbines for energy production constitute the main elements of the process. In-depth data analysis was carried out to determine the control variables and the major issues related to the previous conduction of the plant. A tailored modelization process was conducted, and satisfactory fitting performances were obtained with linear models. In particular, first-principles equations were combined with data-based techniques. The achievement of a reliable model of the plant and the availability of reliable forecasts of the measured disturbance variables—e.g., the hydroelectric power production plan—motivated the choice of a control approach based on model predictive control techniques. A tailored methodology was proposed to account for model uncertainties, and an ad hoc model mismatch compensation strategy was designed. Virtual environment simulations based on meaningful scenarios confirmed the validity of the proposed approach for reducing water waste while meeting the water demand for electric energy production. The control system was commissioned for the real plant, obtaining significant performance and a remarkable service factor.

Restoring the operability of advanced process control systems

Restoring the operability of advanced process control systems

Advanced Motor Control for Improving the Trajectory Tracking Accuracy of a Low-Cost Mobile Robot

Accurate trajectory tracking is a paramount objective when a mobile robot must perform complicated tasks. In high-speed movements, time delays appear when reaching the desired position and orientation, as well as overshoots in the changes of orientation, which prevent the execution of some tasks. One of the aspects that most influences the tracking performance is the control system of the actuators of the robot wheels. It usually implements PID controllers that, in the case of low-cost robots, do not yield a good tracking performance owing to friction nonlinearity, hardware time delay and saturation. We propose to overcome these problems by designing an advanced process control system composed of a PID controller plus a prefilter combined with a Smith predictor, an anti-windup scheme and a Coulomb friction compensator. The contribution of this article is the motor control scheme and the method to tune the parameters of the controllers. It has been implemented in a well-known low-cost small mobile robot and experiments have been carried out that demonstrate the improvement achieved in the performance by using this control system.

Applications of Advanced Process Control Techniques to an Italian Water Distribution Network

In this paper an application of Advanced Process Control techniques to an Italian water distribution network is presented. Several in-depth hydraulic studies had previously been conducted in order to perform hardware modifications through sectorization procedures. In order to further improve performance, both pressure management of the water distribution system and optimization of pump scheduling problems have been addressed. Net pressure has been minimized through two-layer Model Predictive Control techniques, while advanced logics have been designed for the pumps scheduling. The developed Advanced Process Control system has been successfully installed allowing to reduce water losses and to achieve a significant reduction of operational costs in terms of electric energy and fault rates.

(Digital Presentation) An Advanced Process Control Scheme for Fin Etching Process in Front-End-of-Line FinFET High Volume Production

As FinFET scales down to advanced technology nodes, Fin profile and Critical Dimension (CD) control are becoming a huge challenge due to the increased number of processes. For a stable wafer-to-wafer CD performance, Advanced Process Control (APC) system is widely deployed in high volume manufacturing production. In this work, we present a novel implementation of an APC-based fuzzy control system in the Fin etching process within the widely-used Self-aligned Quadruple Patterning (SaQP) scheme. Feed-forward APC is applied to the two processes: 1) Atomic Layer Deposition (ALD) process for fin hard-mask etching and 2) fin etching process with fuzzy control. With the deployment of the APC system, the inline metrology variation performance of Fin CD improved from ±3.5% to ±1.8%. The Cp and Cpk are increased by 29% and 32%, for various key inline parameters. The APC-based fuzzy control scheme shows great potential for the advanced nodes.

Industry 4.0 in Action: Digitalisation of a Continuous Process Manufacturing for Formulated Products

The pharmaceutical industry is going through a significant change to adopt smart manufacturing for more integrated supply chains and improved sustainability. Today's competitive market demands have put pressure on healthcare systems to take a comprehensive assessment of the drug life cycle, its environmental effect, industrial use of energy and resources, supply chain, and impact on end-users. The exploitation of emerging Industry 4.0 technologies will allow a sustainable process design and personalised health care system through the realisation of digital twins, which could transform the pharmaceutical sector to be more flexible, robust, adaptive, and smart. A significant level of research and development has been applied to pharmaceutical manufacturing especially in existing, outdated design and scale-up paradigms in isolated unit operations. However, addressing the key challenges in pharmaceutical manufacturing requires whole systems approaches to incorporate Industry 4.0 concepts.This paper aims to share the latest development of an advanced digital twin of a continuous wet granulation and tableting process at The University of Sheffield. These include the delivery of a digital platform consisting of an Advanced Process Control system (APC), mechanistic model platform and industrial IoT platform for data analytics and visualisation. The combined solution aligns with the concepts of Industry 4.0 by providing a digital twin, cloud integration, sophisticated statistical, as well as hybrid and mechanistic models. The models are in turn, used for soft-sensors, Model Predictive Control and Optimisation algorithms to predict and control product Quality Attributes. The potential application of digital twins in the pharmaceutical industry will also be explored.

Advanced Process Control Applications to Water Resources Systems: Two Industrial Case Studies

This paper presents two industrial case studies where Advanced Process Control techniques have been applied. The first case study is represented by a hydroelectric plant located in Italy. In this plant, a model predictive controller has been designed and installed, in order to optimally satisfy the production plan. The predictive control approach has been motivated by the availability of forecasts on water requests by the hydroelectric plant and by the goodness of fit performances of the obtained linear process model. The second case study is represented by a pumping station located in an Italian Water Distribution Network. Advanced control logics have been designed in order to optimally and automatically manage the scheduling of the involved pumps. The developed Advanced Process Control systems have been successfully installed on the real plants, obtaining energy efficiency and process control improvement.

Enhancement of biological nutrient removal process with advanced process control tools in full-scale wastewater treatment plant

One of the major challenges in existing WasteWater Treatment Plants (WWTPs) is to comply with the increasingly stringent nutrient discharge limits established by the competent authorities to enhance environmental protection, while keeping operation costs as low as possible. This paper describes the results obtained from upgrading a full-scale WWTP during seven years (2013–2020) applying five different Advanced Process Control (APC) strategies. Results show that implementation of APC and the development of ammonia-based aeration control, aeration/non-aeration cycles, improved internal/external recirculation and chemical dosage controls resulted in an improvement in nutrients removal rates (+25.48% and +9.25%, for nitrogen and phosphorus, respectively) and in a reduction (-21.79%) of the specific energy ratio. In addition, the promotion of an Enhanced Biological Phosphorous Removal (EBPR) process by APC resulted in an improvement in biological phosphorous removal (+43.90%), chemical savings (-20.00%) and nutrient recovery in the dewatered sludge. Molecular biology tools and laboratory batch tests confirmed the Polyphosphate Accumulating Organisms (PAOs) activity, specifically Tetrasphaera genera. Finally, an economic analysis was conducted, showing a rate of return for the incurred capital expenses with the implemented APC systems of about five years, being an affordable alternative to the upgrading existing WWTPs.

Построение регрессионных уравнений для виртуальных анализаторов основных показателей качества процесса алкилирования

The growth prospects of the chemical and petrochemical industries are shaped by the necessity of developing and implementing advanced technological solutions for improving operational performance of production by the effect of selecting the most suitable process parameters and predicting quality factors of the end products once very minute. The task of continuous product quality control may be successfully resolved by means of implementing an Advanced Process Control System (APCS). The functioning of APCS is based on virtual analyzers (VA) of product quality. A virtual analyzer is a mathematical model which is being developed with reference to long-time statistics of lab collected and performance data. Virtual analyzers facilitate assessing the desired, rather than directly measured, product quality factors by product parameters which are under continuous control by means of advanced control systems. The article describes an approach to developing a system of virtual analyzers for target quality factors of alkylphenols based on regression equations that have been formulated by the least-squares method.

ОПТИМИЗАЦИЯ ПРОЦЕССА ПОЛУЧЕНИЯ ВОДОРОДА ИЗ ПРИРОДНОГО ГАЗА

The paper discusses a plant for the production of hydrogen from natural gas and ways to optimize the technological process. A schematic diagram of section 4100 of the installation for the production of hydrogen-containing gas (HCG) with hydrogen concentration of 99.9% is presented. The control tasks of the hydrogen production unit during deep oil refining using an advanced process control system (APCS) are defined. The results of simulation of the APCS controller functioning at external disturbance are presented. An algorithm for the automatic regulation of loading of section 4100 is proposed

Application of adaptive models in advanced mass transfer process control systems

Application of adaptive models in advanced mass transfer process control systems

Control of the temperature in a petroleum refinery heating furnace based on a robust modified Smith predictor

The robust control of the crude oil outlet temperature uniformity in a heating furnace of a petroleum refinery is addressed. A reliable dynamic model of the nominal process has been attained using a system identification procedure based on real-time data. This procedure yields a second order model with a dominant time-delay. A PI controller embedded in a modified Smith predictor structure is therefore proposed. Sensitivity and robustness properties of this control system are analytically obtained. Based on that, a tuning procedure is developed for this control system which has lower sensitivity to disturbances than the standard Smith predictor while guaranteeing the system stability when plant parameters change. Simulations are carried out of the proposed control system and other well-known linear advanced process control systems. The comparison of the obtained results shows the superior performance of our control system in most cases, both in rejecting a variety of disturbances and in maintaining the closed-loop stability when the process parameters change.

Advanced Process Control: From a PID Loop up to Refinery-Wide Optimization

Advanced process control (APC) provides distributed control systems with wider functionality thus improving operations efficiency. At the same time, APC systems create a framework for higher-level optimization solutions. The paper outlines the key features and advantages of Honeywell Profit® Suite product family enabling effective and user-friendly design of layered optimization solutions. With a case study of a major Russian refinery it overviews the development and operation history of advanced control (AC) and optimization solutions from a PID loop up to a group of interrelated process units.