Data-driven Soft Sensors in the Process Industry

In the last two decades Soft Sensors established themselves as a valuable alternative to the traditional means for the acquisition of critical process variables, process monitoring and other tasks which are related to process control. This paper discusses characteristics of the process industry data which are critical for the development of data-driven Soft Sensors. These characteristics are common to a large number of process industry fields, like the chemical industry, bioprocess industry, steel industry, etc. The focus of this work is put on the data-driven Soft Sensors because of their growing popularity, already demonstrated usefulness and huge, though yet not completely realised, potential. A comprehensive selection of case studies covering the three most important Soft Sensor application fields, a general introduction to the most popular Soft Sensor modelling techniques as well as a discussion of some open issues in the Soft Sensor development and maintenance and their possible solutions are the main contributions of this work

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested

Application of AI in Chemical Engineering

A major shortcoming of traditional strategies is the fact that solving chemical engineering problems due to the highly nonlinear behavior of chemical processes is often impossible or very difficult. Today, artificial intelligence (AI) techniques are becoming useful due to simple implementation, easy designing, generality, robustness and flexibility. The AI includes various branches, namely, artificial neural network, fuzzy logic, genetic algorithm, expert systems and hybrid systems. They have been widely used in various applications of the chemical engineering field including modeling, process control, classification, fault detection and diagnosis. In this chapter, the capabilities of AI are investigated in various chemical engineering fields

Distillation

The purpose of this book is to offer readers important topics on the modeling, simulation, and optimization of distillation processes. The book is divided into four main sections: the first section is introduction to the topic, the second presents work related to distillation process modeling, the third deals with the modeling of phase equilibrium, one of the most important steps of distillation process modeling, and the the fourth looks at the reactive distillation process, a process that has been applied successfully to a number of applications and has been revealed as a promising strategy for a number of recent challenges

Application of Computational Intelligence Techniques to Process Industry Problems

In the last two decades there has been a large progress in the computational intelligence research field. The fruits of the effort spent on the research in the discussed field are powerful techniques for pattern recognition, data mining, data modelling, etc. These techniques achieve high performance on traditional data sets like the UCI machine learning database. Unfortunately, this kind of data sources usually represent clean data without any problems like data outliers, missing values, feature co-linearity, etc. common to real-life industrial data. The presence of faulty data samples can have very harmful effects on the models, for example if presented during the training of the models, it can either cause sub-optimal performance of the trained model or in the worst case destroy the so far learnt knowledge of the model. For these reasons the application of present modelling techniques to industrial problems has developed into a research field on its own. Based on the discussion of the properties and issues of the data and the state-of-the-art modelling techniques in the process industry, in this paper a novel unified approach to the development of predictive models in the process industry is presented

Adaptif Neurofuzzy Inference System Untuk Pengukuran Ph

Due to increasing of measurement performance necessity and pH controlling on laboratory scale and industry, it needs to make measurement system that can give the best performance with the high accuracy and robust with the disturbance and noise. Sensor of pH measurement is combination electrode with voltage output. This is influenced by hydrogen ion and liquid temperature. It will lead error if there is temperature changing in the measured process. In order to solve this problem, it needs pH measurement device that can measure pH without disturbed by temperature changes. ANFIS (Adaptive Neuro Fuzzy Inference System) can be used for identifying the voltage due from hydrogen ion and temperature changes. So, the pH measurement would be robust to temperature changes. Based on this result study, precision and accuracy device are 1.91% and 0.45% in the pH range 2-10.6 and temperature range 10-80oC. This gives conclusion that ANFIS can decrease pH reading error because of temperature changes

The predictive functional control and the management of constraints in GUANAY II autonomous underwater vehicle actuators

Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group's interests. In this paper, we focus on the predictive functional control (PFC), which is a control strategy that is easy to understand, install, tune, and optimize. PFC is being developed and applied in industrial applications, such as distillation, reactors, and furnaces. This paper presents the rst application of the PFC in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared with other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point, stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this paper on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.Peer ReviewedPostprint (author's final draft

Neuro-Fuzzy Controller forMethanol Recovery Distillation Column

Distillation columns are widely used in chemical processes as separation systems in industries. In order to gain better product quality and lower the energy consumption of the distillation column, an effective control system is needed to allow the process to be operated over larger operating ranges. In this study Different control strategies were used to control the distillate and bottom compositions of a packed distillation column to separate the mixture of methanol (CH3OH) and water (H2O). The tuning of control parameters were determined for PI and PID controllers using three different methods; Internal Model Control (IMC), Ziegler-Nichols (Z.N), and Cohen-Coon (PRC) to find the best values of proportional gain (KC), integral time (τI) and derivative time (τD). The Internal Model Control (IMC) method gave better results than that of the other two methods thus it was recommended to be the tuning method in this work. The low values of ITAE of 61.3 for distillate product composition and 54 for bottom composition were obtained which represent the adaptive neuro-fuzzy inference system (ANFIS) method and assure the feasibility of this method as a control strategy among other methods (conventional feedback controllers (PI, PID), artificial neural network (ANN) , adaptive fuzzy logic and PID fuzzy logic controllers)

An Investigation On Imc Based Fuzzy Pid Controllers

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 2010Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 2010Bu çalışmada, Dahili Model Kontrol Temelli Bulanık PID Kontrol Ediciler (DMKTBPID) için birtakım öz ayar kuralları ve Çok Bölgeli Öz Ayar Yöntemi önerilmiştir. Geliştirme işlemine temel olarak yakın geçmişte Bulanık PID kontrol ediciler için önerilmiş olan Dahili Model Kontrol yöntemi incelenmiştir. Bu kontrol stratejisinin performansı klasik PID kontrol edici ile kıyaslanmıştır. Bu çalışmalarda, DMKTBPID, klasik PID kontrol ediciye göre daha iyi sonuçlar vermiştir fakat bu kontrol edicinin yüksek mertebeli veya yüksek zaman gecikmeli sistemlerin kontrolü için birtakım geliştirmelere ihtiyaç duyduğu gözlenmiştir. Birtakım öz ayar kuralları oluşturulmuştur ve bu kurallar, Öz Ayarlı DMKTBPID kontrol ediciler kullanılarak zaman sabiti ve zaman gecikmesi çok geniş menzillerde değişen farklı proseslerin başarıyla kontrol edilebilmesi için gerekli öz ayar algoritmaları ve katsayılarını içermektedir. Gerçekleştirilen bir dizi simülasyon çalışması sonucunda elde edilen sonuçlara göre, Çok Bölgeli Öz ayarlı DMKTBPID kontrol edicinin öz ayarsız klasik DMKTBPID kontrol ediciye göre çok daha iyi performans sergilediği sonucuna ulaşılmıştır. Özellikle çok yüksek zaman gecikmesine sahip proseslerin kontrolünde Çok Bölgeli Öz Ayarlı DMKTBPID kontrol edicinin uzak ara daha başarılı sonuçlar sağladığı gözlenmiştir.In this study, certain self tuning algorithms and Multi-Region Self Tuning Method for Fuzzy IMC PID controllers have been proposed. As basis, recently proposed IMC based Fuzzy PID controller tuning technique is investigated. The performance of Fuzzy IMC PID controller has been compared with that of classical PID controller. Fuzzy IMC PID controller has demonstrated better results in general but seemed to need further improvement in controlling high order and high delay time processes. Some self tuning rules have been prepared and these rules include necessary self tuning algorithms and coefficients for controlling various kinds of processes, whose time delay and time constant properties vary in a very large range, by using Self Tuning Fuzzy IMC PID controller. Simulation results showed that, proposed Multi-Region Self Tuning Fuzzy IMC PID controller provided better results for all kinds of processes compared to Non-self tuning Fuzzy IMC PID controller. Especially for very high time delay processes, Multi-Region Self Tuning Fuzzy IMC PID performance was far more successful than that of its non-self tuning counterpart.Yüksek LisansM.Sc

A Review on AI Control of Reactive Distillation for Various Applications

In this chapter, previous studies on reactive distillation process control including control using conventional as well as soft sensor control, membrane assisted reactive distillation design and simulation, estimation and control are discussed. The review of literature in different dimensions is carried out to explore the opportunities in the field of research work. The chapter is focused on dynamics and control of Reactive distillation, its control using Conventional Techniques, Model Predictive Control MPC), Reactive Distillation using Soft Sensors/Soft Controllers, Membrane assisted reactive distillation, Biodiesel in Reactive Divided Wall Column: Design and Control and Membrane reactive divided wall column. These control techniques are proposed and analyzed by many researchers. These techniques have potential use in process industries to have better soft sensor control of nonlinear processes