Progress and challenges in control of chemical processes

Hybrid Controller Based on Numerical Methods for Chemical Processes with a Long Time Delay

A hybrid control framework is proposed as an alternative for long time delays in chemical processes. The hybrid approach mixes the numerical methods in an internal mode control (IMC) structure, which uses the particle swarm optimization (PSO) algorithm to improve the adjustment of the controller parameters. Simulation tests are carried out on linear systems of high order and inverse response, both with dominant delay, and tests on a nonlinear process (chemical reactor). The performance of the proposed controller is stable and satisfactory despite nonlinearities in various operating conditions, set-point changes, process disturbances, and modeling errors. In addition, experimental tests were performed on a setup composed of two heaters and two temperature sensors mounted on an Arduino microcontroller-based board called the Temperature Control Laboratory (TCLab), with an additional software delay introduced. The merits and drawbacks of each scheme are analyzed using radar charts, comparing the control methods with different performance measures for set-point and disturbance changes. Furthermore, the new controller uses PSO to improve the tuning parameters.

Dual-Mode Based Sliding Mode Control Approach for Nonlinear Chemical Processes

This paper synthesizes a new sliding mode controller (SMC) approach to enhance tracking and regulation tasks by following dual-mode concepts. The new control law consists of two distinct types of operation, using the combination of higher gain to large error signals (transient) and lower gain to small error signals (the region around the set point). The design is presented from a dual-mode (PD-PID) sliding surface operating in concert, fulfilling desired control objectives to ensure stability and performance. Therefore, a new controller was established, and we called it a dual-mode based SMC. The proposed controller is tested by computer simulations applied to two nonlinear processes, a continuous stirred-tank reactor (CSTR) and a mixing tank with a variable dead time. The results are compared with two different alternatives of SMC. In addition, the merits and drawbacks of the control schemes are analyzed using radial graphs, comparing the control methods with various performance measures for set points and disturbances changes. The ITSE (integral of time multiplied by the squared error), TVu (total variation of control effort) indices, Mp (maximum overshoot), and ts (settling time) were the indices used for performance analysis and comparisons.

Robust control designs for microalgae cultivation in continuous photobioreactors

Microalgae are used to produce renewable biofuels and high-value components and in bioremediation and CO2 sequestration tasks. These increasing applications, in conjunction with a desirable constant large-scale productivity, motivate the development and application of practical controllers. This paper addresses the application of robust control schemes for microalgae cultivation in continuous photobioreactors. Due to the model uncertainties and external perturbations, robust control designs are required to guarantee the desired microalgae productivity. Furthermore, simple controller designs are desirable for practical implementation purposes. Therefore, two robust control designs are applied and evaluated in this paper for two relevant case studies of microalgae cultivation in photobioreactors. The first control design is based on an enhanced simple-input output model with uncertain estimation. The second control design is the robust nonlinear model predictive control considering different uncertain scenarios. Numerical simulations of two case studies aimed at lipid production and CO2 capture under different conditions are presented to evaluate the robust closed-loop performance.

Robust model-based control of a packed absorption column for the natural gas sweetening process

The sweetening units are the most important in natural gas processing. Packed bed absorption columns are widely used in the sweetening process; however, their operation and control are not simple due to their highly non-linear behavior derived from their distributed nature and interaction between multiple physical phenomena. In this work, two robust model-based control schemes are implemented to regulate the CO2 concentration at the outlet of a packed bed absorption column in the gas sweetening process. The model of an industrial-scale absorption column and the structure of the controllers, i) control based on modeling error compensation (MEC) ideas, and ii) nonlinear model predictive control (NMPC) are described. Numerical results show that the proposed robust model-based controllers can regulate the controlled variable to the desired reference despite external disturbances, set-point changes, and uncertainties in the absorption column model.

The importance of fungal pathogens and antifungal coatings in medical device infections

In recent years, increasing evidence has been collated on the contributions of fungal species, particularly Candida, to medical device infections. Fungal species can form biofilms by themselves or by participating in polymicrobial biofilms with bacteria. Thus, there is a clear need for effective preventative measures, such as thin coatings that can be applied onto medical devices to stop the attachment, proliferation, and formation of device-associated biofilms. However, fungi being eukaryotes, the challenge is greater than for bacterial infections because antifungal agents are often toxic towards eukaryotic host cells. Whilst there is extensive literature on antibacterial coatings, a far lesser body of literature exists on surfaces or coatings that prevent attachment and biofilm formation on medical devices by fungal pathogens. Here we review strategies for the design and fabrication of medical devices with antifungal surfaces. We also survey the microbiology literature on fundamental mechanisms by which fungi attach and spread on natural and synthetic surfaces. Research in this field requires close collaboration between biomaterials scientists, microbiologists and clinicians; we consider progress in the molecular understanding of fungal recognition of, and attachment to, suitable surfaces, and of ensuing metabolic changes, to be essential for designing rational approaches towards effective antifungal coatings, rather than empirical trial of coatings.

Effects of wireless packet loss in industrial process control systems

Timely and reliable sensing and actuation control are essential in networked control. This depends on not only the precision/quality of the sensors and actuators used but also on how well the communications links between the field instruments and the controller have been designed. Wireless networking offers simple deployment, reconfigurability, scalability, and reduced operational expenditure, and is easier to upgrade than wired solutions. However, the adoption of wireless networking has been slow in industrial process control due to the stochastic and less than 100% reliable nature of wireless communications and lack of a model to evaluate the effects of such communications imperfections on the overall control performance. In this paper, we study how control performance is affected by wireless link quality, which in turn is adversely affected by severe propagation loss in harsh industrial environments, co-channel interference, and unintended interference from other devices. We select the Tennessee Eastman Challenge Model (TE) for our study. A decentralized process control system, first proposed by N. Ricker, is adopted that employs 41 sensors and 12 actuators to manage the production process in the TE plant. We consider the scenario where wireless links are used to periodically transmit essential sensor measurement data, such as pressure, temperature and chemical composition to the controller as well as control commands to manipulate the actuators according to predetermined setpoints. We consider two models for packet loss in the wireless links, namely, an independent and identically distributed (IID) packet loss model and the two-state Gilbert-Elliot (GE) channel model. While the former is a random loss model, the latter can model bursty losses. With each channel model, the performance of the simulated decentralized controller using wireless links is compared with the one using wired links providing instant and 100% reliable communications. The sensitivity of the controller to the burstiness of packet loss is also characterized in different process stages. The performance results indicate that wireless links with redundant bandwidth reservation can meet the requirements of the TE process model under normal operational conditions. When disturbances are introduced in the TE plant model, wireless packet loss during transitions between process stages need further protection in severely impaired links. Techniques such as retransmission scheduling, multipath routing and enhanced physical layer design are discussed and the latest industrial wireless protocols are compared.

Case Studies in Modelling, Control in Food Processes

This chapter discusses the importance of modelling and control in increasing food process efficiency and ensuring product quality. Various approaches to both modelling and control in food processing are set in the context of the specific challenges in this industrial sector and latest developments in each area are discussed. Three industrial case studies are used to demonstrate the benefits of advanced measurement, modelling and control in food processes. The first case study illustrates the use of knowledge elicitation from expert operators in the process for the manufacture of potato chips (French fries) and the consequent improvements in process control to increase the consistency of the resulting product. The second case study highlights the economic benefits of tighter control of an important process parameter, moisture content, in potato crisp (chips) manufacture. The final case study describes the use of NIR spectroscopy in ensuring effective mixing of dry multicomponent mixtures and pastes. Practical implementation tips and infrastructure requirements are also discussed.

Big Data Analytics in Chemical Engineering

Big data analytics is the journey to turn data into insights for more informed business and operational decisions. As the chemical engineering community is collecting more data (volume) from different sources (variety), this journey becomes more challenging in terms of using the right data and the right tools (analytics) to make the right decisions in real time (velocity). This article highlights recent big data advancements in five industries, including chemicals, energy, semiconductors, pharmaceuticals, and food, and then discusses technical, platform, and culture challenges. To reach the next milestone in multiplying successes to the enterprise level, government, academia, and industry need to collaboratively focus on workforce development and innovation.