Industrial Process Control Research Articles

Nanotechnology-Enabled Biosensors: A Review of Fundamentals, Design Principles, Materials, and Applications.

Biosensors are modern engineering tools that can be widely used for various technological applications. In the recent past, biosensors have been widely used in a broad application spectrum including industrial process control, the military, environmental monitoring, health care, microbiology, and food quality control. Biosensors are also used specifically for monitoring environmental pollution, detecting toxic elements' presence, the presence of bio-hazardous viruses or bacteria in organic matter, and biomolecule detection in clinical diagnostics. Moreover, deep medical applications such as well-being monitoring, chronic disease treatment, and in vitro medical examination studies such as the screening of infectious diseases for early detection. The scope for expanding the use of biosensors is very high owing to their inherent advantages such as ease of use, scalability, and simple manufacturing process. Biosensor technology is more prevalent as a large-scale, low cost, and enhanced technology in the modern medical field. Integration of nanotechnology with biosensors has shown the development path for the novel sensing mechanisms and biosensors as they enhance the performance and sensing ability of the currently used biosensors. Nanoscale dimensional integration promotes the formulation of biosensors with simple and rapid detection of molecules along with the detection of single biomolecules where they can also be evaluated and analyzed critically. Nanomaterials are used for the manufacturing of nano-biosensors and the nanomaterials commonly used include nanoparticles, nanowires, carbon nanotubes (CNTs), nanorods, and quantum dots (QDs). Nanomaterials possess various advantages such as color tunability, high detection sensitivity, a large surface area, high carrier capacity, high stability, and high thermal and electrical conductivity. The current review focuses on nanotechnology-enabled biosensors, their fundamentals, and architectural design. The review also expands the view on the materials used for fabricating biosensors and the probable applications of nanotechnology-enabled biosensors.

Radioactive Waste Management in a Medical Cyclotron Facility - A Review

The cyclotron is a device used to create radioactive atoms with a short half-life (radioactive isotopes) that can be utilised for research and medical imaging. When nuclear and radiation facilities are utilized, serviced, or decommissioned, radioactive waste is produced. The amount of radioactive waste produced is greatly decreased by good operating procedures. Iodine-123, Technetium-99m, Iodine-131, Gallium-67 Thallium-201 and fluorine-18 fluorodeoxyglucose are among the radionuclides utilised in medicine. The most widely used gaseous/aerosol radionuclides are (aerosolized) technetium-99m, xenon-133, and krypton-81m. The use of radionuclides (radioactive element) for industrial process control and instrumentation, medical diagnostic and therapeutic purposes, as well as numerous uses in research, education, agriculture, geological exploration, construction, and other human endeavors, results in radioactive waste. These applications generate a variety of radioactive waste, which can come from sealed sources and be in solid, liquid, or gaseous form. If the trash containing considerable amounts of radionuclides is not handled properly, there may be serious concerns to both the environment and human health. Due to the wide variety of waste kinds addressed, special consideration must be paid to safety concerns and regulatory management. This article will examine the fundamental procedures for managing radioactive waste in compliance with the regulatory agencies like AERB (Atomic Energy Regulatory Board) and IAEA (International Atomic Energy Agency).

Predicting the bandgap energy of distorted GaSbxAs1-x and InSbxAs1-x using design of experiment (DoE) and artificial intelligence (AI): A comparative study

The band anticrossing (BAC) theory is widely used to model the bandgap energy of GaSbxAs1-x and InSbxAs1-x materials and is based on two effects: impurity-host interaction in the range rich in antimony and arsenic content, and intraband coupling within the conduction and valence bands in the moderate range. In this study, the response surface method (RSM) was applied to the data and an attempt was made to develop an artificial neural network (ANN) based on the Levenberg-Maquardt backpropagation algorithm to predict the bandgap energy in a more precise manner. The predicted results were verified using experimental data. Using R2 and RMSE, we found that the neural network model provided more accurate losses, with correlation coefficients of 99.28% and 97.42% for GaSbAs and InSbAs, respectively. Subsequently, we exploited the predicted values of the bandgap energy to study the effect of the lattice mismatch on the optoelectronic properties of the materials. We have shown that the non-degenerate bandgap energies of the deformed layer associated with heavy holes (hh) and light holes (lh) calculated from the values predicted by the ANN undergo a highly nonlinear variation compared to their BAC and RSM counterparts, and the energy range narrows with increasing Sb(x) content. These materials are of scientific interest for infrared photodetectors, which are used in a wide variety of applications including medical diagnostics, thermal imaging for industrial process control, and military applications.

Development of liquid level measurement technology: A review

Depending on various applications, the process control industry uses liquid-level sensors of different principles. Due to the dynamic nature of liquid such as water, wastewater, petroleum etc., and characteristic of storage tank, the measurement is difficult and it violate the accurate measurement. Instances of level measurement and control failure lead to tragic and costly consequences. Selecting the accurate level measurement technique for a particular application is a daunting task in the face of many options and limitations. Hence, this paper reviews liquid level measuring instruments used in the past 11 years. Both the contact and non-contact measurement are discussed, and their development on measuring liquid level is also covered in this review paper. From the study, it is clear that ultrasonic sensors are most widely used for liquid level measurement with high accuracy while neglecting the drawback of the echo inside the tank.

False data injection attacks detection based on Laguerre function in nonlinear Cyber‐Physical systems

This paper focus on the detection for false data injection (FDI) attacks. Under external conditions, the state and the output variables of Nonlinear Cyber‐Physical Systems (NCPSs) cannot be described by a linear relationship. It is difficult to detect the attacked systems. The Laguerre function model has excellent approximation capability to change structural parameters such as system delay and order, making it convenient for online parameter identification and suitable for complex industrial process control. Accordingly, in order to detect FDI attacks for NCPSs, the Laguerre function is adopted to detect FDI attacks and improve the detection rate. Numerical simulations verify the effectiveness of this method.

A 12-bit Digital-to-Analog Converter for Current Distribution Type

Sensors have a wide range of applications in environmental monitoring, for example, as temperature transmitters in industrial control processes. In order to reduce the chip area and improve the digital-to-analogue conversion accuracy, large loads are driven. This study proposes a current-shunted DAC for sensor interfaces with an R-2R structure, using a Class-AB amplifier to buffer or amplify the signal. Spectre simulation shows that the DAC has a current consumption of 63uA when the supply voltage and reference voltage are 1.8V and can drive a 15pF capacitive load.

Optofluidic Fabry-Perot interferometric sensor for the real-time measurement of refractive index

In this work, a microfluidic system combined with a fibre-optic extrinsic Fabry-Perot interferometer is proposed to measure refractive index continuously and in real time. A microfluidic platform was designed and created for this purpose through 3D printing. The Fabry-Perot cavity is an integral part of the microfluidic chip and is perpendicular to the sample flow. The light is conducted through a single mode optical fibre and the refractive index measurements were based on the optical power and wavelength shift of the reflected spectra. The developed optofluidic setup was characterised using different concentrations of glucose solutions. A sensitivity of 1102 nm/RIU was obtained when using the wavelength shift, however, when the same solution was analysed over time, the signal for wavelength shift measurements was found to be unstable. The optical power shift was correlated with the refractive index and a sensitivity of -79.6 dB/RIU was obtained, with a good linearity (r2 = 0.996). Good results were verified in terms of stability with a maximum standard deviation of 0.028 dB and a sensor resolution of 4.3×10−4 RIU. This sensor has a great potential for applications in which refractive index real-time measurements are required, such as food and beverages industry process control.

Case Studies and Best Practices in Cloud-Based Big Data Analytics for Process Control

In this research paper, case studies and exemplars and lessons learnt in cloud-based big data analytics for process control are reviewed. The paper presents big data, cloud computing and industrial process control system with prospects of enhancing effectiveness, increasing production rates, and effective decision making in the industries. The research in this paper involves a comprehensive literature review of the research topic, and an extension of the analysis to four specific business industries as well as a discussion of architectural elements for cloud-based big data solutions for process control business. It also presents various crucial issues such as data protection, adherence to legal requirements, and compatibility with other systems, giving solutions. In addition, the research compares the effectiveness of cloud-based solutions with on-premise ones and discuss other novelties, including edge computing and artificial intelligence as the tendencies potentially influencing process control. Consequently, the findings of this research can be helpful for both industry practitioners and researchers who aim to optimize process control and organization operation with the help of cloud-based big data analytics

Research on Identification Algorithm of Cascade Control System

Aiming at the problem of object model identification of modern industrial process control systems, a new closed-loop moment parameter identification online method based on the data of normal operation of the running system is proposed. In this method, only one step response data of the system is required, and appropriate convergence factors are introduced into the Laplace formula, the trapezoidal integral method is used to calculate the values of two derivatives of the transfer function, then the four unknown parameters of the second-order model can be solved by fitting the data with the least square method, and the target model can be identified. Finally, the simulation results of building different objects through Matlab show that the identification method has general applicability and good robustness with high recognition, and it is not sensitive to noise signals.

Energy-saving optimization on active disturbance rejection decoupling multivariable control

An industrial control process multiple-input multiple-output (MIMO) coupled system is analyzed in this study as an example of a Loss of Coolant Accident (LOCA) simulation system. Ordinary control algorithms can complete the steady state of the control system and even reduce the response time to some extent, but the entire system still consumes a large amount of energy after reaching the steady state. So a multivariable decoupled energy-saving control method is proposed, and a novel energy-saving function (economic function, Eco-Function) is specially designed based on the active disturbance rejection control algorithm. Simulations and LOCA simulation system tests show that the Eco-function algorithm can cope with the uncertainty of the multivariable system's internal parameters and external disturbances, and it can save up to 67% of energy consumption in maintaining the parameter steady state.

Adaptive Internal Model Control Based on Parameter Adaptation

In actual industrial control, many objects have the characteristics of large lag and time-varying parameters, which makes it difficult for traditional PID control to obtain satisfactory control effects. Therefore, engineers need a control algorithm with a better control effect and a simple structure, and thus the internal model control is introduced into industrial process control. Internal model control has received a lot of attention in the control field because of its excellent control effect, and in ideal conditions, it can perfectly suppress external disturbances, and the output strictly tracks the input. On the basis of the internal model control, a two-degrees-of-freedom internal model control was developed. Compared with the conventional feedback control, the internal model control structure mainly embeds an internal model consistent with the control object in the control object, so the deviation between the internal model and the control object determines the quality of the control effect. In this paper, the internal model is changed to an adjustable parameter model, and the model parameters are adjusted in real time using the parameter adaptive algorithm, so that the model output error is as small as possible, or even zero. In order to solve the influence of interference on the system, compensation based on MRAC theory is used. In order to verify the feasibility of the algorithm, it was applied to the landing process control of fixed-wing Unmanned aerial vehicle) UAV and achieved satisfactory results.

Imaged capillary isoelectric focusing (icIEF) tandem high resolution mass spectrometry for charged heterogeneity of protein drugs in biopharmaceutical discovery

Since the first commercial imaged capillary isoelectric focusing (icIEF) instrument was developed twenty years ago, the technology has become the gold standard of quality and manufacturing process control in the biopharmaceutical industry. This is owing to its high-resolution and high-throughput characterization of protein charge heterogeneity. In addition to a charge variant profiling, mass spectrometry (MS) analyses are also desirable to obtain an in-tact molecular weight (MW) and further identification of these charged species. While offline fractionation technologies including isoelectric focusing (IEF) and free flow electrophoresis (FFE) followed by liquid chromatography (LC)-mass spectrometry (MS) coupling have been employed for this purpose, there have been much fewer reported applications of icIEF-based MS connection and fraction collection. Factors that have impeded the development of these icIEF applications include difficulties with a direct connection to the MS interface as well as high background signal of carrier ampholytes and incompatible coated capillary cartridges. In this work, we developed a robust and flexible icIEF-MS platform which overcomes these challenges to achieve both the rapid icIEF separation and high-resolution MS (HRMS) identification of protein charged variants simultaneously. We demonstrate how this methodology proves highly-sensitive and highly reliable for the characterization of commercial monoclonal antibodies (mAbs) and antibody-drug-conjugates (ADCs). The whole workflow of icIEF-MS for protein heterogeneity is straight forward and accurate and can be performed within 45 min. Furthermore, the developed icIEF-MS configuration can flexibly switch to icIEF-based fraction collection model allowing the user to perform additional in-depth characterization such as peptide mapping by high performance liquid chromatography (HPLC) tandem mass spectrometry (LC-MS/MS).

Stabilizing region in dominant pole placement based discrete time PID control of delayed lead processes using random sampling

Handling time delays in industrial process control is a major challenge in the dominant pole placement based design of proportional-integral-derivative (PID) controllers due to variable number of zeros and poles which may arise from the Pade approximation of the exponential delay terms in the characteristic polynomials used for stability analysis. This paper proposes a new concept for designing PID controllers with a derivative filter using dominant pole placement method mapped onto the discrete time domain with a suitable choice of the sampling time to convert the continuous time time-delays into finite number of discrete time poles. Here, the continuous-time plant and the filtered PID controller have been discretized using the pole-zero matching method for handling linear dynamical systems, represented by the first order plus time delay with zero (FOPTDZ) transfer function models of the open-loop system under control. We use a swarm intelligence based global optimization method as a sampler to discover the approximate the pattern of the stabilizable region in the controller parameter as well as the design specification space while also satisfying the analytical conditions for pole placement given as higher order polynomials. Simulations on test-bench plants with open-loop stable, unstable, integrating, low-pass, high-pass characteristics have been presented in order to demonstrate the validity and effectiveness of the proposed control design method.

Coordination‐behavior patterns of control crews in digital nuclear power plants during emergencies from a network perspective: An exploratory study

AbstractThis study aims to investigate the coordination‐behavior patterns of control crews in digital nuclear power plants (NPPs) during emergencies from a network perspective. We observed and coded 12 coordination processes (each from one crew) of handling simulated emergencies on a full‐scope dynamic simulator in Tianwan NPP of China. By calculating the proportion of coordination breakdowns and referring to the subjective evaluation of instructors, these control crews (all male) were classified into two performance levels (high and low). To compare the coordination‐behavior patterns between high‐ and low‐performing crews, we conducted social network analysis based on the number and direction of coordination behaviors. By examining intracrew relations in team coordination, this study indicated that high‐performing crews exhibited higher cohesion and more balanced behavioral patterns in team coordination than low‐performing ones. By investigating coordination‐behavior patterns from the hierarchical structure of relations in the team, this study revealed that high‐performing crews exhibited higher degree of team autonomy and self‐management among junior operators, as well as reactor operator (RO)‐centered pattern, whereas the low‐performing crews exhibited senior reactor operator‐centered pattern. For researchers, the results advance the understanding of the team coordination mechanism in NPP control rooms during emergencies and enrich team coordination theory in process control industries from the network perspective. In practice, this study suggested that the team coordination training of NPP control crews might be centered around junior operators (especially RO), with focuses on facilitating information sharing and mutual assistance between junior operators and enhancing the awareness of active cooperation of crew members. The results of the study provide nuclear instructors with practical reference to team coordination training and processes optimization.

Development and control of a virtual industrial process for assembling computers

A virtual manufacturing method represents the emerging approach that allows companies to improve their processes in order to introduce new products more quickly to their markets in a cost-effective manner. This involves building a synthetic and integrated environment based on software tools and systems, such as Virtual Reality and Simulation, to facilitate such processes. This paper presents the development of virtual industrial environment, highlighting the development and control of virtual industrial process for assembling computers by combining two simulation software tools. Development of industrial process was performed using Factory I/O software tool, while the control program was developed using FluidSim software tool. This paper also highlights the usage of OPC communication protocol, that was used for communication between software tools during simulation.

The multipole resonance probe: simultaneous determination of electron density and electron temperature using spectral kinetic simulation

The multipole resonance probe (MRP) is an innovative sensor suitable for monitoring and control of industrial plasma processes. It is a realization of ‘active plasma resonance spectroscopy’, a class of electromagnetic diagnostic methods which employ the ability of plasmas to resonate on or near the plasma frequency. A signal in the GHz range is fed to the plasma via an electrical probe; the spectral response S(ω) is recorded, and then evaluated with a mathematical model to obtain information on the internal plasma parameters. In this study, a spectral kinetic model of the MRP is discussed. It is superior to previous analyses based on the Drude model, as it allows to determine not only the electron density n e but also the electron temperature T e from S(ω). Good agreement with independent measurements shows the suitability of the model.

Accelerating reinforcement learning with case-based model-assisted experience augmentation for process control

In the context of intelligent manufacturing in the process industry, traditional model-based optimization control methods cannot adapt to the situation of drastic changes in working conditions or operating modes. Reinforcement learning (RL) directly achieves the control objective by interacting with the environment, and has significant advantages in the presence of uncertainty since it does not require an explicit model of the operating plant. However, most RL algorithms fail to retain transfer learning capabilities in the presence of mode variation, which becomes a practical obstacle to industrial process control applications. To address these issues, we design a framework that uses local data augmentation to improve the training efficiency and transfer learning (adaptability) performance. Therefore, this paper proposes a novel RL control algorithm, CBR-MA-DDPG, organically integrating case-based reasoning (CBR), model-assisted (MA) experience augmentation, and deep deterministic policy gradient (DDPG). When the operating mode changes, CBR-MA-DDPG can quickly adapt to the varying environment and achieve the desired control performance within several training episodes. Experimental analyses on a continuous stirred tank reactor (CSTR) and an organic Rankine cycle (ORC) demonstrate the superiority of the proposed method in terms of both adaptability and control performance/robustness. The results show that the control performance of the CBR-MA-DDPG agent outperforms the conventional PI and MPC control schemes, and that it has higher training efficiency than the state-of-the-art DDPG, TD3, and PPO algorithms in transfer learning scenarios with mode shift situations.

Communication Times Reconstruction in a Telecontrolled Client–Server Scheme: An Approach by Kalman Filter Applied to a Proprietary Real-Time Operating System and TCP/IP Protocol

Nowadays, various systems were developed in the telecommunications field which make use of technologies for the transmission and reception of information. One of these technologies is the Internet, which was developed in tandem with scientific growth. Therefore, its application in the control of various industrial processes has a notable influence. In this context, there are industrial processes that, due to the potential danger they represent to human beings, must be controlled by means of a remote control system. Such systems can be implemented through client–server communication schemes, which form a network of computers to exchange information. In the exchange of information, delay times are generated. These inactivity times have a close relationship with the latency in the communication network and have a negative impact on the performance of closed-loop control systems. In this sense, for physical implementation, it is essential to measure and mathematically characterize their magnitudes in order to know their variability and thus be able to design control strategies that compensate for their effects. Hence, this research paper presents the reconstruction of the communication times measured from a telecontrol system, where it is assumed that only one subsystem acts as the controller and the other one acts as the controlled. In other words, this paper addresses a control scheme type of single-input-single-output system (SISO). This reconstruction is based on the Kalman filter, which estimates the communication times that are measured on an experimental test bench with a client–server communication scheme. Communication times are characterized as stochastic processes. So, in order to validate the reconstruction presented, the level of dependence between the random processes is evaluated by analyzing their moments of probability as well as their covariance moments. Finally, an analysis based on the mean square error is presented, through which it can be concluded that the reconstruction technique used allows one to know the dynamics of the communication times generated by the remote control process presented in this research.

Acetate accumulation and regulation by process parameters control in Chinese hamster ovary cell culture.

Chinese hamster ovary (CHO) cells represent a group of predominantly used mammalian hosts for producing recombinant therapeutic proteins. Known for theirrapid proliferation rates, CHO cells undergo aerobic glycolysis that is characterized by fast glucose consumption, that ultimately gives rise to a group of small-molecule organic acids. However, only the function of lactate has been extensively studied in CHO cell culture. In this study, we observed the accumulation of acetate from the late exponential phase to harvest day, potentially contributing to the pH decline in late culture stage regardless of lactate consumption. In addition, we evaluated the acidification of the fresh media and the cell culture suspension, and the data revealed that acetate presented a lower acidification capacity compared to lactate and exhibited limited inhibitory effect on cells with less than 20 mM supplemented in the media. This study also explored the ways to control acetate accumulation in CHO cell culture by manipulating the process parameters such as temperature, glucose, and pH control. The positive correlation between the specific glucose consumption rate and acetate generation rate provides evidence of the endogenous acetate generation from overflow metabolism. Reducing these parameters (temperature, glucose consumption) and HCl-controlled low pH ultimately suppress acetate build-up. In addition, the specific acetate generation rate and relevant glucose consumption rate are found to be a metabolic trait associated with specific cell lines. Taken together, the results presented in these experiments provide a means to advance industrial CHO cell culture process control and development.

A Single-Phase Compact-Sized Matrix Converter with Symmetrical Bipolar Buck and Boost Output Voltage Control

The development of single-phase symmetrical bipolar voltage gain matrix converters (MC) is growing rapidly as they find their application in power systems for dynamic restoration of line voltages, high voltage AC–DC converters, and variable frequency controllers for many industrial processes. However, the existing trend in matrix converter technology is a buck–boost operation that has inherently serious issues of high voltage and current surges or stresses. This is a big source of the high voltage and current rating of semiconductor switching devices. There is also a problem of high ripples both for voltage as well for current, requiring large size of filtering capacitors and inductors. The non-symmetrical control of the voltage gain increases the control complication. A large count of operating transistors is critical regarding their cost, size, and power conversion losses, as the space and cost required by their gate control circuits are much larger than the size and cost of the switching transistors. Thus, in this research work, a new single-phase MC is introduced only employing six fully controlled switching devices, ensuring similar operation or outputs as is obtained from the existing topologies that require the use of eight or more fully controlled switching devices, and the reduction by two or more switching transistors helps to compact the overall size and lower the overall cost. The separation in its voltage buck and boost operation enables smooth control of the voltage gain through duty cycle control. The low values of the voltage and current surges reduce the power rating and losses of the switching devices. The flow of the current in the filtering inductor is kept unidirectional to avoid the current interruption and reversal problem once the operation of the converter is abruptly switched from inverting to non-inverting and vice versa. All these factors are comprehensively detailed through the circuit’s description and comparative analysis. Simulation and practical results are presented to confirm the effectiveness of the developed circuit topology.