Industrial Control Applications Research Articles

Water-soluble sulfonated polyaniline as multifunctional scaling inhibitor for crystallization control in industrial applications

The assessment of the water-soluble sulfonated polyaniline (SPANI) as a new scale inhibitor of CaCO3 and CaSO4 using slandered NACE, electrochemical tests and microscopic examinations was the main objective of this work. The adsorption properties of the scale inhibitor were investigated using adsorption isotherms. Results showed that SPANI is an efficient new inhibitor in controlling the precipitation of calcium carbonate and calcium sulfate scales. The inhibitory effect of SPANI can be attributed to interaction of calcium with the polymer backbone via (Ca…SO3) coordination which disturb the normal crystal growth of scales on solutions. The molecular interaction constant depends on charges at the hydrophilic sulfonates head as well as steric entanglement of polymer chains. Furthermore, adsorption isotherms prove that the inhibitory effect of SPANI can be also attributed to the presence of negative charge on the polymer backbone which facilitates the attachment of polymer molecules on metal surface which hinder attachment of scale crystals on it. It can be concluded that SPANI alters the growth mechanisms of the crystals in solutions, as well as it change the potential of scales to attach equipment surface.

Guest Editorial: Industrial Cyber–Physical Systems—New Trends in Computing and Communications

The papers in this special section focus on industrial cyber-physical systems (CPS), with an emphasis on computing and communications applications. CPS systems are defined by integrating computation and communication facilities on the one hand and the monitoring and control of physical processes on the other hand. Industrial cyber–physical systems (ICPS) refer to the science and art of designing and using cyber–physical systems for industrial and process control applications, for example in smart factories, smart energy grids, smart transportation systems, smart cities, and several other areas. Many of these applications are time- and mission-critical and hence often require low latency and high reliability. In parallel, there has been a strong growth in integrating intelligence, often in the form of machine/deep learning, into applications, which also leads to vastly increasing computational requirements. These innovations then will be integrated into complex systems, which need to be properly engineered to become safe, reliable, trustworthy, and secure while at the same time being cost-efficient.

Performance Evaluation with Multivariable and Multiloop control for a class of square MIMO systems

Two approaches (centralized and decentralized) for designing a multi-input, multi-output (MIMO) tracing/regulating process are described within article. The vast popular of industrial process control applications are whist focused on multi loop controllers, ignoring the feedback control attainment with advantages of centralized multivariable controllers. Due to their single loop nature, the plant interactions that are merely in use keen on relation in the controller tuning process cannot be suppressed by decentralized controllers. In many situations, therefore it would be beneficial to delimit the detrimental consequences in pairing among inputs and outputs of the closed loop system under certain context. The centralized model predictive controllers (MPC) and decentralized/multi-loop PI controllers are designed. In terms of integral Absolute Error (IAE), Integral Square Error (ISE), and Integral Time-weighted Absolute Error (ITAE), the output of both controllers is then compared. The results of the simulation showed that the MIMO MPC is better than the other suggested control schemes. The projected central controllers minimize interactions superior than the multi loop controllers that have recently been published.

TiN NPs: a novel ethanol gas sensing material with ultrafast response and good stability

TiN, as a typical representative of transition metal nitrides (TMNs), has many excellent properties (higher electrical conductivity and chemical inertness). These features are of great importance for developing novel resistive chemical gas sensing. Here, we presented a facile ambient pressure chemical vapor deposition (APCVD) for the synthesis of TiN nanoparticles (NPs). Our result shows that TiN NPs exhibit excellent gas sensing behaviors towards ethanol gas. The high response and good stability towards ethanol gas was realized for the first time. Moreover, in order to elucidate the sensing mechanism, the carrier concentration and mobility in air and ethanol gas was studied for TiN NPs, offering a direct evidence for explaining the sensing behavior. Interestingly, the carrier concentrations play an important role in fast response ethanol gas. This study reveals that TiN could be a promising candidate for ethanol sensing, opening up new directions for applications in industrial control, environmental safety and other fields.

Titanium dioxide (TiO2) nanoparticles reinforced polyvinyl formal (PVF) nanocomposites as chemiresistive gas sensor for sulfur dioxide (SO2) monitoring

The present work reports the preparation of polyvinyl formal (PVF)/Titanium dioxide (TiO2) nanocomposite films using a solution casting method followed by the characterization of the synthesized PVF/TiO2 nanocomposite films using various analytical techniques namely FTIR, XRD, UV–vis, SEM and TGA analysis. The results obtained from different analyses confirmed that the TiO2 NPs was fine dispersed within the PVF matrix and there exists well compatibility among the polymer matrix and the nanofiller. The pristine TiO2 NPs based fabricated chemiresistive sensor exhibits the maximum sensitivity of 50.25% at 370 °C where as PVF/TiO2 nanocomposite sensor showed the enhanced sensitivity of 83.75% at a relatively low operating temperature of 150 °C towards 600 ppm sulfur dioxide (SO2) gas. The 25 wt% PVF/TiO2 nanocomposite film sensor exhibited good sensitivity (∼83.75%), selectivity, rapid response time (66 s)/recovery time (107 s), and long-term stability of 60 days for SO2 gas detection. The fabricated PVF/TiO2 nanocomposite film sensors in our work possesses the advantages of low power consumption, cost-effective, and distinguished sensing abilities for SO2 detection makes it possible for potential applications. Thus, the fabricated chemiresistive sensors based on TiO2 NPs reinforced PVF nanocomposites films are evaluated and experimental results to show an excellent behavior towards SO2 gas detection for industrial processes control and environmental monitoring applications.

Broadband laser-based mid-IR spectroscopy for analysis of proteins and monitoring of enzyme activity

Laser-based infrared (IR) spectroscopy is an emerging key technology for the analysis of solutes and for real-time reaction monitoring in liquids. Larger applicable pathlengths compared to the traditional gold standard Fourier transform IR (FTIR) spectroscopy enable robust measurements of analytes in a strongly absorbing matrix such as water. Recent advancements in laser development also provide large accessible spectral coverage thus overcoming an inherent drawback of laser-based IR spectroscopy.In this work, we benchmark a commercial room temperature operated broadband external cavity-quantum cascade laser (EC-QCL)-IR spectrometer with a spectral coverage of 400 cm−1 against FTIR spectroscopy and showcase its application for measuring the secondary structure of proteins in water, and for monitoring the lipase-catalyzed saponification of triacetin. Regarding the obtained limit of detection (LOD), the laser-based spectrometer compared well to a research-grade FTIR spectrometer employing a liquid nitrogen cooled detector. With respect to a routine FTIR spectrometer equipped with a room temperature operated pyroelectric detector, a 15-fold increase in LOD was obtained in the spectral range of 1600–1700 cm−1. Characteristic spectral features in the amide I and amide II region of three representative proteins with different secondary structures could be measured at concentrations as low as 0.25 mg mL−1. Enzymatic hydrolysis of triacetin by lipase was monitored, demonstrating the advantage of a broad spectral coverage for following complex chemical reactions. The obtained results in combination with the portability and small footprint of the employed spectrometer opens a wide range of future applications in protein analysis and industrial process control, which cannot be readily met by FTIR spectroscopy without recurring to liquid nitrogen cooled detectors.

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science.

Linear complementarity model predictive control with limited iterations for box-constrained problems

This paper presents a sufficient condition and its certification algorithm for solving box-constrained linear model predictive control (MPC) problems within a certain number of iterations equal to the number of constrained prediction points. For implementing a model predictive controller in industrial applications, it is crucial to guarantee a concrete upper bound on the computational load so that the processor specifications can be determined. To address this matter, we transform the constrained quadratic optimization of MPC into an equivalent linear complementarity problem (LCP). Then, we show that if a modified n-step vector exists for a given problem, the corresponding LCP is feasible and solvable within a predetermined number of iterations, each comprising fundamental arithmetic operations. The existence of a modified n-step vector is independent of the current states and box constraint bounds. Thus, the total computational load required to solve an MPC problem can be explicitly determined. In addition, an explicit certification algorithm for checking the existence of a modified n-step vector is proposed. Utilizing this algorithm, the existence range of a modified n-step vector is investigated for various problem configurations. Numerical tests show that the computational speed of the proposed method is competitive with those of commonly available algorithms.

A new reference model-based fixed-structure controller for industrial applications

A new reference model-based fixed-structure controller for industrial applications

RETRACTED: Simulation of football based on PID controller and BP neural network

Wheel robot soccer speed control system using a ball object detection method and PID controller. A control system is based on the object detection system's behavior based on the robot position's orientation to the target position. PIDs are instruments, pressure, speed, and other operational factors used in control, temperature adjustment flow, and industrial control applications. The PID controller uses control loop feedback dynamics to control functional variables and is the most accurate and stable controller. The robot position is held by placing the ball vertically. When the robot's work is perpendicular to the ball, the robot moves with a certain speed controlled by the PIT controller based on the robot's distance and the ball. Standard conditions (standard ball) test results show that the robot can detect the ball material while in the vertical position, whether on the robot's right or left. In the random test that changes direction, the robot can move more dynamically as the ball's change in place.

Evaluating the Fitness of Combinations of Adsorbents for Quantitative Gas Sensor Arrays.

Robust, high-performance gas-sensing technology has applications in industrial process monitoring and control, air quality monitoring, food quality assessment, medical diagnosis, and security threat detection. Nanoporous materials (NPMs) could be utilized as recognition elements in a gas sensor because they selectively adsorb gas. Imitating mammalian olfaction, sensor arrays of NPMs use measurements of the adsorbed mass of gas in a set of distinct NPMs to infer the gas composition. Modular and adjustable NPMs, such as metal-organic frameworks (MOFs), offer a vast material space to sample for combinations to comprise a sensor array that produces a response pattern rich with information about the gas composition. Herein, we frame quantitative gas sensing, using arrays of NPMs, as an inverse problem, which equips us with a method to evaluate the fitness of a proposed combination of NPMs in a sensor array. While the (routine) forward problem is to use an adsorption model to predict the mass of gas adsorbed in each NPM when immersed in a gas mixture of a given composition, the inverse problem is to predict the gas composition from the observed masses of adsorbed gas in the NPMs of the array. The fitness of a given combination of NPMs for gas sensing is then determined by the conditioning of its inverse problem: the prediction of the gas composition provided by a fit (unfit) combination of NPMs is insensitive (sensitive) to inevitable errors in the measurements of the mass of gas adsorbed in the NPMs. For illustration, we use experimentally measured adsorption data to analyze the conditioning of the inverse problem associated with a (IRMOF-1 and HKUST-1) CH4/CO2 sensor array.

RETRACTED: Development of circular economy in smart cities based on FPGA and wireless sensors

Smart City is an urban environment that combines advanced wireless sensors, information, and communication technology to help effectively manage the city's assets. Wireless Sensor is a rapidly evolving technology into a variety of applications. Wireless sensors are used to detect, calculate, measure, and monitor environmental elements in the communication network, and infrastructure, logging, and characterization profile generating node and improve the medical, transportation, agriculture, industrial process control applications many great potentials, and the economic growth in smart cities. The sensor is an important part of smart cities, as they are based on data collected for these services. Wireless Sensors can transmit data of various remote locations, maybe a relatively inexpensive and compact sensor with low power consumption and maintenance costs, allowing them to build deployment. The Implementation of wireless sensors for smart cities' economic growth of wireless sensors is used for smart building, health monitoring, surveillance, and avoid traffic congestion. Improved transportation, communication technology, avoiding traffic congestion, national infrastructure, including social infrastructure and network components, protocols, and sensor deployment, to achieve the industry constraints, has been designed to ensure reliability and low power consumption.

Low-Temperature Hydrogen Sensor: Enhanced Performance Enabled through Photoactive Pd-Decorated TiO2 Colloidal Crystals.

The high demand for H2 gas sensors is not just limited to industrial process control and leak detection applications but also extends to the food and medical industry to determine the presence of various types of bacteria or underlying medical conditions. For instance, sensing of H2 at low concentrations (<10 ppm) is essential for developing breath analyzers for the noninvasive diagnosis of some gastrointestinal diseases. However, there are major challenges to overcome in order to achieve high sensitivity and hence low limit of detection (LoD) toward H2. In this study, it is demonstrated that light-assisted amperometric gas sensors employing sensitive layers based on Pd-decorated TiO2 long-range ordered crystals can achieve excellent H2 sensing performance. This unique combination of materials and novel layered structure enables the detection of H2 gas down to 50 ppm with highly promising LoD capabilities. The sensor response profiles revealed that the sensor's signal-to-noise ratio was higher in the presence of light when operated with a 9 V bias (relative to other conditions used), producing a LoD of only 3.5 ppm at an operating temperature of 33 °C. The high performance of the sensor makes it attractive for applications that require low-level (ppm as opposed to conventional % levels) H2 gas detection. Most importantly, the developed sensor exhibited high selectivity (>93%) toward H2 over other gas species such as CO2, C4H8O, C3H6O, CH3CHO, and NO, which are commonly found to coexist in the environment.

Time-Critical Wireless Networked Embedded Systems: Feasibility and Experimental Assessment

In this article, we investigate an innovative solution, to implement high sampling frequency industrial control by means of networked embedded systems connected via WiFi. The basic idea relies on a co-design approach for the control application, which is then able to adapt its sampling period, as well as to tune the Wi-Fi parameters, according to the feedback coming from the network. To this end, we implemented a cross-layer architecture acting at both application and data-link layers, which features a robust frame-delay state estimator, a time-efficient communication policy, and a specific tuning of the critical protocol parameters. Suitable hardware-in-the-loop experiments have been carried out exploiting two different embedded systems available off-the-shelf. The preliminary results, obtained from an extensive experimental campaign, are encouraging since they show that the proposed architecture enables industrial control applications requiring a sampling rate up to 1000 Hz, even in presence of communication impairments.

MPC model monitoring and diagnosis for non-square systems

Many industrial model predictive control applications, called non-square systems, have more variables to be controlled than manipulated variables available. At these cases, the control objectives are related to keep the controlled ones within a range, instead in reference value (setpoint). Assessing the model quality of these controllers is fundamental, however, most available MPC assessment methods are setpoint dependent, providing misleading results when these are unavailable. The methods proposed by Botelho et al. (2015, 2016a, 2016b) allow the model assessment of linear MPCs independently from the setpoints. Even though such methodologies have this advantage, there is not a procedure to assess this kind of controller. Therefore, this paper presents a guideline to apply Botelho et al. (2015, 2016a, 2016b) methods in non-square MPCs. The Shell Heavy Oil Process is used as a case study. The results demonstrate that the procedure is capable of estimate the effect of modeling problems and indicate the associated controlled variable, as well as whether the problem is due to a model-plant mismatch or unmeasured disturbance in non-square controllers.

A Comprehensive Dependability Model for QoM-Aware Industrial WSN When Performing Visual Area Coverage in Occluded Scenarios.

In critical industrial monitoring and control applications, dependability evaluation will be usually required. For wireless sensor networks deployed in industrial plants, dependability evaluation can provide valuable information, enabling proper preventive or contingency measures to assure their correct and safe operation. However, when employing sensor nodes equipped with cameras, visual coverage failures may have a deep impact on the perceived quality of industrial applications, besides the already expected impacts of hardware and connectivity failures. This article proposes a comprehensive mathematical model for dependability evaluation centered on the concept of Quality of Monitoring (QoM), processing availability, reliability and effective coverage parameters in a combined way. Practical evaluation issues are discussed and simulation results are presented to demonstrate how the proposed model can be applied in wireless industrial sensor networks when assessing and enhancing their dependability.

ASiMOV: A self-protecting control application for the smart factory

The evolution of manufacturing systems into a smart factory brings advantages but also increased cyber-risks. This paper investigates the problem of intrusion detection and autonomous response to cyber-attacks targeting the control logic of industrial control applications for the smart factory. Specifically, we propose ASiMOV (Asynchronous Modular Verification), a self-protecting architecture for cyber–physical systems realizing a verifiable control application. ASiMOV is inspired by modular redundancy and leverages virtualization technologies to respond and to prevent cyber-attacks to the control logic. Using simulation experiments, we evaluate: the effects of an attack on an industrial control application enhanced by ASiMOV; the delay introduced by ASiMOV within a control loop; and the cyber-attack detection delay. Results show that, in the simulated scenario, the controller can work with a sampling rate of up to 200 Hertz. Any tampering with the control logic is detected without false positives/negatives in a time equal to the latency between the proposed control application and the proposed IDS (e.g., tens to hundreds of milliseconds).

Unraveling Complexity: A Strategy for the Characterization of Anisotropic Core Multishell Nanoparticles

AbstractIn this work, a widely applicable routine to characterize the core, surface, stability, and optical properties of CdSe/CdS/ZnS core–shell–shell nanorods after multiple growth steps is established. First, size, shape, and shell thickness of the nanorods are characterized by transmission electron microscopy (TEM), analytical ultracentrifugation (AUC), and small angle X‐ray/neutron scattering (SAXS/SANS). In the next step, Fourier‐transform infrared (FT‐IR) spectroscopy, thermogravimetric analysis (TGA), and SANS measurements are applied to determine the surface species of nanorods. Then, the colloidal stability of the nanorods is investigated by UV–vis spectroscopy and dynamic light scattering (DLS) after different washing cycles. Finally, photoluminescence quantum yield (PLQY) of the nanorods during washing and sample storage is determined. With this highly complementary routine for particle characterization, the core, surface, stability, and optical properties of nanorods after multiple growth steps are resolved. The results demonstrate the importance of the developed toolbox to characterize such highly complex, anisotropic nanorods for a technical environment. This is of major importance for the handling of colloidal quantum materials and their quality control in industrial applications.

Explicit lot inspection by attributes using minimal prior information

An explicit optimal attribute sampling plan for lot acceptance with maximum allowable expected risks is presented without specifying the full prior model of the defective rate. The suggested methodology to find an approximate solution of the underlying integer nonlinear programming problem is simple, fast and sufficiently accurate in most practical cases. The smallest number of units to be inspected per lot in order to reach the required protections for customers and manufacturers, as well as the maximum tolerable number of defectives in the selected sample, is derived in closed-form using Taylor series expansions of the operating characteristic function. The proposed approach allows the practitioners to determine a nearly optimal inspection scheme using only prior estimations of the means and variances of the defective rates for the acceptable and rejectable lots. Furthermore, it provides easy ways to adequately combine multiple expert opinions and to update the current optimal test plan when new information becomes available. The incorporation of prior knowledge yields considerable savings in sample size, as well as improved evaluations of the current expected producer and consumer risks. Some applications in industrial reliability and quality control are included for illustrative and comparative purposes.

Secure Links: Secure-by-Design Communications in IEC 61499 Industrial Control Applications

Increasing automation and external connectivity in industrial control systems (ICS) demand a greater emphasis on software-level communication security. In this article, we propose a secure-by-design development method for building ICS applications, where requirements from security standards like ISA/IEC 62443 are fulfilled by design-time abstractions called secure links. Proposed as an extension to the IEC 61499 development standard, secure links incorporate both light-weight and traditional security mechanisms into applications with negligible effort. Applications containing secure links can be automatically compiled into fully IEC 61499-compliant software. Experimental results show secure links significantly reduce design and code complexity and improve application maintainability and requirements traceability.