Advanced Process Control Techniques Research Articles

Advanced Process Control for Clinker Rotary Kiln and Grate Cooler

The cement industry includes energy-intensive processes, e.g., clinker rotary kilns and clinker grate coolers. Clinker is obtained through chemical and physical reactions in a rotary kiln from raw meal; these reactions also involve combustion processes. The grate cooler is located downstream of the clinker rotary kiln with the purpose of suitably cooling the clinker. The clinker is cooled through the action of multiple cold air fan units as it is transported within the grate cooler. The present work describes a project where Advanced Process Control techniques are applied to a clinker rotary kiln and a clinker grate cooler. Model Predictive Control was selected as the main control strategy. Linear models with delays are obtained through ad hoc plant experiments and suitably included in the controllers' formulation. A cooperation and coordination policy is introduced between the kiln and the cooler controllers. The main objectives of the controllers are to control the rotary kiln and grate cooler critical process variables while minimizing the fuel/coal specific consumption of the kiln and the electric energy consumption of the cold air fan units within the cooler. The overall control system was installed on the real plant, obtaining significant results in terms of service factor and control and energy-saving performances.

Advanced Process Control Strategy for a Condensate Stabilization Unit: Energy, Exergy, Economic, and Environmental (4E) Study

This study assesses the energy, exergy, economic, and environmental performance of a gas stabilization unit by employing a new strategy to implement an advanced control system. The advanced process control (APC) strategy is established based on the response surface methodology to assess the real-time performance of the process. The RSM-based APC technique targets the energy, exergy, economic, and environmental (4E) performance of the process in unsteady-state operations. A detailed sensitivity analysis is also conducted to evaluate the relative significance of operating parameters on the objective functions, such as exergy efficiency, energy cost, and CO2 emission. The RSM-based APC strategy proposes an algorithm that can readily be implemented in the plant to increase the plant’s energy performance. Implementing this intelligent control system leads to the production of standard products, while production cost, energy consumption, exergy efficiency, and environmental impacts are enhanced. The exergy and sensitivity results indicate that the stabilization tower has the highest potential for process enhancement, and reboiler temperature is the most influential factor among operating parameters. Also, the RSM-based APC improves the exergy efficiency of the plant by 19% and reduces the energy cost by 13%. At the optimal state, 457 kg/h CO2 emission is prevented.

Energy Consumption Analysis of a Continuous Flow Ohmic Heater with Advanced Process Controls

This paper presents an analysis of the energy consumption of a continuous flow ohmic heater (CFOH) with advanced process controls for heating operations in the food and drinks industry. The study was carried out by using operational data collected from a CFOH pilot plant that was designed and constructed at the National Centre of Excellence for Food Engineering (NCEFE), Sheffield Hallam University. The CFOH is controlled by a PC and includes an onboard Programmable Logic Controller (PLC) and a Human Machine Interface (HMI) so that it can be operated as a stand-alone unit with basic on/off and power setting control but without any advanced control features. The technical solution presented in this paper for heating foods demonstrates significant energy saving compared with conventional heating methods. Using the CFOH, the electric current generated in the food products by the Joule effect produces a rapid temperature increase with very high energy efficiency. This technique eliminates the low efficiency of heat transfer from the surface of vessels typically used to heat and cook food products. The analysis presented in this paper describes the energy consumption of the CFOH and compares the efficiency of the CFOH when different advanced process control techniques are used. Experimental results and analysis have shown that the CFOH can achieve an energy efficiency conversion of at least 87.9%. It has also shown that the energy conversion percentage can be increased by applying advanced controllers such as model predictive control (MPC) or adaptive model predictive control (AMPC).

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.

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.

Advanced Process Control Modeling from an Advanced Planning and Scheduling Perspective

Further to basic process control strategies, advanced process control (APC) aims to improve the performance of the control by actuating towards input moves against effects of process disturbances. Typically addressing economic-based goals as objectives, APC includes a wide range of techniques embedded in process control systems generally found in continuous-process industries such as processing of food, fuels, bulk chemicals, metals, minerals, pulp and paper, among others. Different from the well-known APC techniques such as feedforward, decoupling, inferential, constraint control, etc., we address a novel approach to the dependent-independent or controlled-manipulated pairing of variables of the model predictive control (MPC), also known as moving horizon control (MHC). We introduce manipulated variables as batch-processes into the MPC formulation to build the dynamic model with their paired controlled variables. The manipulated-controlled MPC formulation is configured as an advanced planning and scheduling (APS) problem as opposed to a traditional APC problem whereby the manipulated-batch-process variables are considered with relative-time yields representing their finite impulse-responses or differenced step-responses. As we shall show, APC problems can be equally formulated as a batch-process in APS perspective. To eliminate the steady-state offset between the actual controlled value and its target or setpoint, it is well-known to apply bias-updating though other forms of parameter-feedback are possible. Typically, APS applications only employ variable-feedback, i.e., opening or starting inventories, properties, etc., but this alone will not alleviate the steady-state offset. A small and representative example highlights the APC as an APS modeling with parameter feedback.

Construction of software using gain-schedule/auto-adaptive control strategy for automation in drilling fluid production plants

Although many advanced nonlinear process control techniques have been developed over the past decade, classic control based on feedback response still has its place. This is mostly so because feedback empirical control is robust and simple to implement and does not require fancy calculations or high-qualified operational manpower to operate it. This work has developed an application written in LabVIEW® environment capable of doing a fully automated single-input single-output control. Preliminary tests were performed in a drilling fluid production unit, controlling flow rate through manipulation of the pump power engine. In the future, tests in the same plant of pressure control by choke valves manipulation will be performed (as found in rig sites, where wellbore pressure is controlled by manipulation of such valves). The final goal is to implement such software in a real rig site, to help operators in drilling control areas such as flow rate and wellbore pressure. The produced software has embedded three self-developed features: automatic plant identification (API), auto-tuning (ABAP) and controllers auto-switch (CAS). The API determines automatically the linearity of the process determining the empirical parameters according to Sundaresan and Krishnaswamy technique. In sequence, it calculates the parameters for P, PI and PID controllers using Cohen–Coon and Ziegler–Nichols methods. The API method automates the sequence of tests necessary to implement the Sundaresan and Krishnaswamy empirical approach. The ABAP feature based on heuristic rules tunes in real time the controllers’ parameters to optimize its response. The CAS allows automatic switch between controllers and parameters to avoid instability, overshoots and creates a synergy with ABAP feature. The results have shown that the API feature is a good optimizer reducing the invested time to calculate all the parameters, from hours to a few minutes. The CAS results demonstrated an associative property with the ABAP feature to mitigate instabilities and overshoots. Therefore, the preliminary results suggested this software is a unique and important tool to improve performance, profitability and reliability during offshore and onshore drilling operations. Moreover, this application could be used in any industry with an approximate first-order dynamic system due to its robustness and a low human interaction need.

Catalytic Naphtha Reforming: A Novel Control System for the Bench-Scale Evaluation of Commercial Continuous Catalytic Regeneration Catalysts

An automated control system was developed for the bench-scale evaluation of continuous catalytic regeneration (CCR) naphtha reforming catalysts. Deactivation of these catalysts is too rapid for fixed bed operation, and regenerating the catalyst continually is not feasible on bench scale. To emulate the commercial process, a solution was required which would allow catalyst evaluation at a constant research octane number (iso-RON). Because changes are too fast for manual adjustment (especially when using Fischer–Tropsch naphtha), automated adjustment of process conditions was required. This was achieved through the use of simple matrix algebra, semiautomated gas chromatography and on-line near-infrared (NIR) analyses. Commercial catalysts with very small activity and reformate yield differences, could be compared and their RON versus yield correlations determined. Product samples large enough for engine research octane number (RON) analyses could also be collected at steady state. Through the use of multidisciplinary analytical and advanced process control techniques, substantial improvement of bench-scale piloting methodology in naphtha reforming was obtained.

Recent Applications of Advanced Control Techniques in Food Industry

Process control has become increasingly important for the food industry since the last decades due to its capability of increasing yield, minimizing production cost, and improving food quality. New developments for control strategies such as artificial neural networks and model-based controls as well as their applications have brought several new prospects to the food industry. Food processes are mostly nonlinear and show different process dynamics with various raw materials and different processing conditions. Therefore, advanced process control techniques are highly invaluable compared to classical control approaches. In this review, advanced control strategies, particularly model-based controllers, fuzzy logic controllers, and neural network-based controllers, are firstly described with their main characteristics. A number of applications of the advanced control strategies are then discussed according to different food processing industries such as baking, drying, fermentation/brewing, dairy, and thermal/pressure food processing.

Application of Advanced Process Control techniques to a pusher type reheating furnace

In this paper an Advanced Process Control system aimed at controlling and optimizing a pusher type reheating furnace located in an Italian steel plant is proposed. The designed controller replaced the previous control system, based on PID controllers manually conducted by process operators. A two-layer Model Predictive Control architecture has been adopted that, exploiting a chemical, physical and economic modelling of the process, overcomes the limitations of plant operators’ mental model and knowledge. In addition, an ad hoc decoupling strategy has been implemented, allowing the selection of the manipulated variables to be used for the control of each single process variable. Finally, in order to improve the system flexibility and resilience, the controller has been equipped with a supervision module. A profitable trade-off between conflicting specifications, e.g. safety, quality and production constraints, energy saving and pollution impact, has been guaranteed. Simulation tests and real plant results demonstrated the soundness and the reliability of the proposed system.

Using OPC technology to support the study of advanced process control

OPC, originally the Object Linking and Embedding (OLE) for Process Control, brings a broad communication opportunity between different kinds of control systems. This paper investigates the use of OPC technology for the study of distributed control systems (DCS) as a cost effective and flexible research tool for the development and testing of advanced process control (APC) techniques in university research centers. Co-Simulation environment based on Matlab, LabVIEW and TCP/IP network is presented here. Several implementation issues and OPC based client/server control application have been addressed for TCP/IP network. A nonlinear boiler model is simulated as OPC server and OPC client is used for closed loop model identification, and to design a Model Predictive Controller. The MPC is able to control the NOx emissions in addition to drum water level and steam pressure.

Optimizing Diesel Production Using Advanced Process Control and Dynamic Simulation

This paper describes the economical and operational benefits achieved with the use of advanced process control techniques and dynamic simulation applied to a Naphtha Splitter Column. The project consists in optimizing the Diesel blending system of Henrique Lage Refinery (REVAP) located in the state of São Paulo, Brazil. The control strategy was designed to maximize production rate, respecting the operational constraints. The results include an increase in the Naphtha flow stream to the Diesel blending system and improvement of the operational stability, leading to valuable economic gains. The project is also a step forward in the use of Dynamic simulation for modelling and identification, where the simulation models have shown to be representative for the inferential variables integration, adding value to the final result.

Why Some APC Applications Lose Performance Over Time And How You Can Avoid It

For a long time, companies in the Chemical Process Industry have turned to Advanced Process Control (APC) as a means to increase profitability and production capacity. Among the most widely used APC techniques is Model Predictive Control (MPC). Many service and software vendors have become knowledgeable in executing projects and delivering MPC applications throughout a diverse range of processes. The focus of this work is on what happens after project conclusion, when the project team is dismissed and the application is handed over to the customer operations.It has been observed in practice that without proper maintenance, APC applications will tend to progressively lose their ability to capture benefits. This work will present how we developed a program aimed at sustaining the value of our APC applications. The program reflects our experience in supporting APC applications from multiple vendors installed in 11 different polymerization lines in seven different sites. It will be presented which steps were taken in building such program starting with the cause-effect analysis, where we identify the causes for application malfunction, going through the identification of stakeholders, key performance indicators and assignment of roles and responsibilities.Although the outlined program was built based on the characteristics of the plants and APC applications found in the polymers business, it should be equally applicable to other industries. It is also interesting that the program does not depend on the choice of APC vendor.

A Useful Control Model for Tandem Hot Metal Strip Rolling

The tandem hot metal strip rolling process is a highly complex nonlinear system that presents a difficult control challenge. This challenge is exacerbated by the hostile hot metal rolling environment, which precludes the location of sensors to measure variables that are important for control. In addition, the controller must have a structure that offers simplicity of tuning during commissioning by personnel who usually are unfamiliar with advanced process control techniques. Based on our previous work using a state-dependent Riccati equation technique for control of the tandem cold metal rolling process, it is considered that a similar method might also be useful for control of tandem hot strip rolling. For the hot rolling process, the development of a process model in a form that is suitable for control development is a significant and challenging task. This paper describes our work to expand on an initial portion of this model to develop a comprehensive nonlinear model of this process. Based on simulation results, it is determined that the complete model has the potential to be useful in the development of a viable nonlinear control method for significant improvement in the control of the tandem hot rolling process.

An Outline of Advanced Regulation Techniques on PLC Background and Control Systems with Self Tuning Methods Design

Advanced process control techniques contain software packages for advanced control based on mathematical methods. There are not only tools for self tuning, but also tools for manufacture costs optimization and energy savings. APC tools are designed to increase the process capacity, yield and quality of products and for costs savings. Self tuning function has become a standard function of distributed control systems. The idea of the automatic system identification procedure and regulator constants calculation is more than obvious and most of nowadays digital industry regulators and PLCs are provided with some kind of the self tuning constant algorithm. Practical part of the paper deals with design of the control system with PID Self Tuner. There is a description of an implementation of the PID regulator as a function block which can be also used for extension control functions.

An Approach for Factory-Wide Control Utilizing Virtual Metrology

In the semiconductor manufacturing industry, market demands and technology trends drive manufacturers towards increases in wafer size and decreases in device size. Application of factory-wide advanced process control (APC) techniques down to the wafer-to-wafer (W2W) level control capability is becoming the only choice to cope with the demanding situation. However, one of the main limitations in undertaking W2W control is the nonavailability of timely metrology data at the wafer level. Recently virtual metrology (VM) techniques have been proposed to provide timely wafer level metrology data; they have the potential to be used in realizing W2W control. In this paper, the VM approach to W2W control on factory level is described. VM for an individual process is realized by utilizing the preprocess metrology and the process data from the underlying tools that is generally collected in real time for fault detection purposes. The VM implementation for factory-wide run-to-run control brings unique opportunities and issues to the forefront such as dealing with expected lower quality of VM data, coordination between VM modules of cascading processes for better prediction quality, flexibility of the factory-wide controller to accommodate lower quality VM data, dynamic adjustments to the target values of individual processes by the factory-wide controller when using VM data, and dealing with metrology delays at the factory level. Near and long-term solutions are presented to address these issues in order to allow VM to be used today and become an integral part of the factory-wide APC solution for W2W control.

심우주 지구 반사율 측정용 아몬라 가시광 채널의 광학 시스템 제조 및 성능 평가

지구와 태양 사이의 중력 평형점에서 혜일로 궤도로 운용되며, 심우주 반사율(albedo)을 측정코자 하는 EARTHSHINE 미션의 과학적 목적에 최적화된 아몬라 가시광 채널의 성능 인증 모델용 광학 시스템 개발을 완료하였다. 아몬라 광학계 설계 요구사항에 부합하는 설계안과 공차분석 결과를 바탕으로 광학 시스템의 설계, 부품 제작, 결합, 정렬, 성능 검증까지의 전 공정을 순수 국내 개발하였으며 특히 측정된 부품들의 광파면 오차를 결합 및 성능 평가 공정에 역투입하는 신 공법에 의하여 성능 검증을 실시하였다. 최종 결합 및 정렬이 완료되어 측정한 아몬라 광학계의 광파면 오차는 중심 시야에서 RMS 0.091 wave(기준 파장: 632.8nin)이며, Ensquared Energy(EE) 성능은 61.7%(<TEX>$14{\mu}m$</TEX> 이내), 변조전달함수(MTF) 성능은 35.3%(기준 주파수: <TEX>$35.7mm^{-1}$</TEX>)이다. 위 결과들은 아몬라 가시광 채널의 요구사항을 모두 만족시킴으로써 개발된 아몬라 가시광 채널 광학계가 EARTHSHINE 미션의 과학목적을 완수할 수 있음을 증명하였다. The AmonRa instrument, the primary payload of the international EARTHSHINE mission, is designed for measurement of deep space albedo from L1 halo orbit. We report the optical design, tolerance analysis and the optical performance of the breadborad AmonRa imaging channel instrument optimized for the mission science requirements. In particular, an advanced wavefront feedback process control technique was used for the instrumentation process including part fabrication, system alignment and integration. The measured performances for the complete breadboard system are the RMS 0.091 wave(test wavelength: 632.8 nm) in wavefront error, the ensquared energy of 61.7%(<TEX>$in\;14\;{\mu}m$</TEX>) and the MTF of 35.3%(Nyquist frequency: <TEX>$35.7\;mm^{-1}$</TEX>) at the center field. These resulting optical system performances prove that the breadboard AmonRa instrument, as built, satisfies the science requirements of the EARTHSHINE mission.

Improvements in Polysilicon Etch Bias and Transistor Gate Control With Module Level APC Methodologies

The targeting of transistor gate length is a primary driver of device performance. The targeting of the physical gate critical dimension (CD) greatly impacts the electrical gate dimension. Traditionally, continual monitoring and manual offsets to compensate for lithographic and etch equipment variability have been used to control gate CDs. This paper discusses how advanced process control techniques were applied to the 0.13-/spl mu/m polysilicon (poly) patterning process. Both scanner and etch equipment were controlled using a combination of feedforward and feedback loops. As a result, significant engineering labor was saved, and gate CD 3 sigma results improved 12%, correlating to improved device performance and enhanced yield.

A software platform for real-time control and monitoring of a wastewater treatment plant

This paper reports on the design, implementation and real-time operation of advanced process control and monitoring techniques for wastewater treatment plants. The paper presents the development of a software platform and its implementation in a full-scale wastewater treatment plant. The software platform allows the real-time execution of advanced process control techniques as model predictive control, subspace identification, and statistical process analysis using principal component analysis methods. Results obtained by the real-time execution of such algorithms are also presented.

Advanced process control techniques for water treatment using artificial neural networks

Virtually all water utilities are looking at improving the operation of their plants to keep control of costs and to meet stringent water quality regulations. Better process control and automation of the plants can help achieve these goals. However, traditional control techniques such as proportional–integral–derivative (PID) can be inadequate when automating certain water treatment processes such as turbidity, organics, or hardness removal in a clarification process. Advanced process control techniques are alternatives to mitigate this impediment. At the cornerstone of many advanced process control techniques is a model of the process being controlled, which can be developed using artificial neural networks (ANNs). This paper describes various advanced process control techniques, the potentially large role of ANN models in implementing these techniques, and issues and solutions when using ANN in a real-time control system. Key words: artificial neural networks, model-based control, proportional-integral-derivative control, forward and inverse models, direct and indirect control.