The Aquila Optimizer (AO) algorithm is a meta-heuristic algorithm with excellent performance, although it may be insufficient or tend to fall into local optima as as the complexity of real-world optimization problems increases. To overcome the shortcomings of AO, we propose an improved Aquila Optimizer algorithm (IAO) which improves the original AO algorithm via three strategies. First, in order to improve the optimization process, we introduce a search control factor (SCF) in which the absolute value decreasing as the iteration progresses, improving the hunting strategies of AO. Second, the random opposition-based learning (ROBL) strategy is added to enhance the algorithm’s exploitation ability. Finally, the Gaussian mutation (GM) strategy is applied to improve the exploration phase. To evaluate the optimization performance, the IAO was estimated on 23 benchmark and CEC2019 test functions. Finally, four real-world engineering problems were used. From the experimental results in compar... [more]

The ability to take non-invasive Raman measurements presents a unique opportunity to use one Raman probe across multiple vessels in parallel, reducing costs but making measurements infrequent. Under these conditions, infrequent and irregular feedback signals can result in poor closed-loop control performance. This study addressed the issue of infrequent and irregular Raman measurements using a linear dynamic model developed from interpolated data to predict more frequent measurements of the controlled variable. The simulated monoclonal antibody production was sampled hourly with white noise added to the simulated glucose concentration to replicate real Raman measurements. The hourly samples were interpolated into 15 min intervals and a linear dynamic model was developed to predict the glucose concentration at 15 min intervals. These predicted values were then used in a feedback control loop by using model predictive control or a conventional proportional and integral controller to cont... [more]

The diagnosis of rolling bearing faults has become an increasingly popular research topic in recent years. However, many studies have been conducted based on sufficient training data. In the real industrial scene, there are some problems in bearing fault diagnosis, including the imbalanced ratio of normal and failure data and the amount of unlabeled data being far more than the amount of marked data. This paper presents a rolling bearing fault diagnosis method suitable for different working conditions based on simulating the real industrial scene. Firstly, the dataset is divided into the source and target domains, and the signals are transformed into pictures by continuous wavelet transform. Secondly, Wasserstein Generative Adversarial Nets-Gradient Penalty (WGAN-GP) is used to generate false sample images; then, the source domain and target domain data are input into the migration learning network with Resnet50 as the backbone for processing to extract similar features. Multi-Kernel M... [more]

Wind generators have attracted a lot of attention in the realm of renewable energy systems, but they are vulnerable to harsh environmental conditions and grid faults. The influence of the manta ray foraging optimizer (MRFO) on the dynamic performance of the two commonly used variable speed wind generators (VSWGs), called the permanent magnet synchronous generator (PMSG) and doubly-fed induction generator (DFIG), is investigated in this research article. The PMSG and DFIG were exposed to identical wind speed changes depending on their wind turbine characteristics, as well as a dangerous three-phase fault, to evaluate the durability of MRFO-based wind side controllers. To protect VSWGs from hazardous gusts and obtain the optimum power from incoming wind speeds, we utilized a pitch angle controller and optimal torque controller, respectively, in our study. During faults, the commonly utilized industrial approach (crowbar system) was exclusively employed to aid the studied VSWGs in achievi... [more]

The control of robotic manipulators has become increasingly difficult over recent years due to their high accuracy, performance, speed, and reliability in a variety of applications, such as industry, medicine, research, etc. These serial manipulator systems are extremely complex because their dynamic models include perturbations, parametric variations, coupled nonlinear dynamics, and non-modular dynamics, all of which require robust control for trajectory tracking. This paper compares two control techniques: computational torque control (CTC) and sliding mode control (SMC). In this study, the latter was used for a physical robotic arm with six degrees of freedom (DOF) and online experiments were conducted, which have received little attention in the literature. As a result, the contribution of this work was based on the real-time application of this controller via a self-developing interface. The great resilience of sliding mode controllers to disturbances was also demonstrated in this... [more]

This study aimed to evaluate the potential efficacy of Bacillus coagulans BACO-17 in ameliorating body fat accumulation as well as gut microbiota dysbiosis in animals, which were given a high-fat diet to mimic the adverse effect of an unhealthy dietary pattern. Compared with normal control, high-fat consumption resulted in significant (p < 0.05) elevations in weight gain (168%), feed efficiency (176%), visceral fat accumulation (228%), and a lesser total fecal short-chain fatty acids (SCFAs) (−27.5%). A significant shift of fecal Fimicutes:Bacteroidetes ratio from 1.13 to 3.14 was also observed. After 12 weeks of experiment, a supplementation of B. coagulans BACO-17 at high dose (9 log CFU/day) along with a high-fat diet could exert an apparent fat reduction ability by decreasing weight gain (by 23.7%) and visceral fat mass (by 24.0%). It was found that B. coagulans BACO-17 was able to increase fecal SCFA concentrations and revert Fimicutes:Bacteroidetes ratio back to the level... [more]

To better understand how positive bias and deposition pressure affect the plasma flow properties in the deposition chamber during the bias-enhanced MPCVD process, a two-dimensional axisymmetric model based on the discharge mechanism of pure H2 was constructed. The coupling process between different physical field models of the electromagnetic field, plasma, and temperature field in the MPCVD reactor is realized. We studied the influence of positive bias voltage and deposition pressure variation on microwave plasma flow characteristics in the deposition chamber. There was a bias voltage threshold phenomenon in the case of positive bias, and the suitable value range was narrow. Additionally, with the increase in the deposition pressure, the electron temperature in the deposition chamber tends to increase locally and reaches its maximum value when the pressure is approximately 30 torr. It provides new ideas and guidance for optimizing the process parameter setting of the bias-enhanced MPC... [more]

Black shank caused by Phytophthora nicotianae has become a destructive soil-borne disease to different flue-cured tobacco cultivars in Southwest China. The use of biochar amendments for microorganism synergy is a promising effective strategy for P. nicotianae development control. In this study, biochar samples were prepared from tamarisk with different pyrolization temperatures (300−500 °C). The effect of pyrolytic temperatures on the bacteria immobilization efficiency of biochar was investigated. B. subtilis Tpb55 was successfully loaded on different biochars as biocontrol composites. The survival investigation of the inoculum suggested that biochar pyrolized at 300 °C (BC300), with a large pore opening diameter; a greater pore volume exhibited a better Tpb55 immobilization. A pot experiment indicated that Tpb55-loaded BC300 had a more pronounced decrease in the disease severity index of black shank disease and an increase in the soil pH, alkali-hydrolyzable nitrogen, soil-available p... [more]

Networked nonlinear systems (NNSs) have great potential security threats because of malicious attacks. These attacks will destabilize the networked systems and disrupt the communication to the networked systems, which will affect the stability and performance of the networked control systems. Therefore, this paper aims to deal with the resilient control problem for NNSs with dynamically triggering mechanisms (DTMs) and malicious aperiodic denial-of-service (DoS) attacks. To mitigate the impact from DoS attacks and economize communication resources, a resilient dynamically triggering controller (RDTC) is designed with DTMs evolving an adaptive adjustment auxiliary variable. Thus, the resulting closed-loop system is exponentially stable by employing the piecewise Lyapunov function technique. In addition, according to the minimum inter-event time, the Zeno behavior can be excluded. Finally, the merits of the proposed controllers and theory are corroborated using the well-known nonlinear C... [more]

To solve the anti-disturbance control problem of dissolved oxygen concentration in the wastewater treatment plant (WWTP), an anti-disturbance control scheme based on reinforcement learning (RL) is proposed. An extended state observer (ESO) based on the Takagi−Sugeno (T-S) fuzzy model is first designed to estimate the the system state and total disturbance. The anti-disturbance controller compensates for the total disturbance based on the output of the observer in real time, online searches the optimal control policy using a neural-network-based adaptive dynamic programming (ADP) controller. For reducing the computational complexity and avoiding local optimal solutions, the echo state network (ESN) is used to approximate the optimal control policy and optimal value function in the ADP controller. Further analysis demonstrates the observer estimation errors for system state and total disturbance are bounded, and the weights of ESNs in the ADP controller are convergent. Finally, the effec... [more]

This paper studies the voltage stability of the Slovak Republic’s power system (PS) based on an assessment of the PV curves. The PV curve is a tool for assessing voltage stability, and based on its shape, it is possible to determine weak and strong voltage nodes with the possibility of voltage stability reserve quantification. We present an analysis of the influence of transformers with an automatic voltage regulator (AVR) on the shape of the PV curves and on the magnitude of voltage in the PS. In general, the 400 kV/110 kV transformers equipped with AVRs are critical assets for the PS as they address voltage control in the DS. However, in the case of voltage problems in the TS, the AVR function may worsen the voltage situation across the entire PS. Therefore, we closely analyze the negative effects of the AVR on the PS operation. This impact is clearly proved, and recommendations are given for the transmission system operator (TSO) in order to maintain voltage stability. In addition,... [more]

The aim of this work is to evaluate thermal behaviors and develop a soft sensor for online prediction of LOD (loss-on-drying) in the segmented fluidized bed dryer (Seg-FBD) in the ConsiGma25 line, which is regarded as the intermediate critical quality attribute for the final drug product. Preheating and drying experiments are performed and heat transfers and conductions among the Seg-FBD are evaluated based on the temperature measurements from sensors and an infrared thermal camera. A temperature distribution in dryer cells and high heat conductions in walls are found. Considerable heat transfers between the neighboring dryer cells are determined, which equal approximately 7% of the energy provided from the heated air. The cell-to-cell heat transfers are implemented into the heat transfer and drying models of the Seg-FBD. The models are calibrated successively in gPROMS Formulated Products (gFP) and the temperature and LOD errors are less than 2 °C and 0.5 wt.%, respectively. Subsequen... [more]

The objective of this study was to synthesize different polyphenol−corn starch complexes including gallic acid−starch and quercetin−starch by conjugating corn starch with gallic acid and quercetin using the free radical grafting method. This process was effective in enhancing conjugations of starch molecules with gallic acid and quercetin (5.20 and 5.83 mg GAE/g, respectively) and imparted promising antioxidant capacity to the phenolic−starch complexes. Significant interactions between these phenolic compounds and corn starch molecules were revealed with an ultraperformance liquid chromatography electrospray ionization Q-time-of-flight mass spectrometry assay. It was revealed that significantly higher levels of resistant starch in the above gallic−starch and quercetin−starch complex samples (11.6 and 15.3 g/100 g, respectively) together with an obvious reduction in glycemic response (7.9% and 11.8%, respectively) observed over the control. Complex samples functionalized with gallic aci... [more]

Hydrocracking is an energy-intensive process, and its control system aims at stable product specifications. When the main product is diesel, the quality measure is usually 95% of the true boiling point. Constant diesel quality is hard to achieve when the feed characteristics vary and feedback control has a long response time. This work suggests a feedforward model predictive control structure for an industrial hydrocracker. A state-space model, an autoregressive exogenous model, a support vector machine regression model, and a deep neural network model are tested in this structure. The resulting reactor temperature decisions and final diesel product quality values are compared against each other and against the actual measurements. The results show the importance of the feed character measurements. Significant improvements are shown in terms of product quality as well as energy savings through decreasing the heat duty of the preheating furnace.

This article addresses the decentralized multi-performance (MP) fuzzy control problem of nonlinear large-scale descriptor (LSD) systems. The considered LSD system contains several subsystems with nonlinear interconnection and external disturbances, and the Takagi−Sugeno fuzzy model (TSFM) is adopted to represent each nonlinear subsystem. Based on the proportional-plus-derivative state feedback (PDSF) scheme, we aim to design a decentralized MP fuzzy controller that guarantees the stabilization, mixed H∞, and passivity performance control (MHPPC), and the guaranteed cost control (GCC) performance of the closed-loop Takagi−Sugeno LSD (TSLSD) systems. Furthermore, we introduce the Lyapunov stability theory and the free-weighting matrix scheme to analyze the stability of the TSLSD system. The proposed sufficient conditions can be transformed as linear matrix inequality (LMI) forms through Schur’s complement, which can be easily solved with the LMI Toolbox. Finally, to illustrate the propos... [more]

With the continuous expansion of industrial production scale, most of the chemical process variables are nonlinear, multi-modal and dynamic. For some traditional multivariate statistical monitoring and fault diagnosis algorithms, such as principal component analysis (PCA), the premise of its application is that the process data is time-independent. To this end, a dynamic principal component analysis (DPCA) method is proposed. However, since the input matrix of DPCA fault diagnosis needs to add an augmented matrix to the original data matrix, the number of eigenvalues of the augmented matrix is too large and there are many redundant eigenvectors. Therefore, this paper proposes a fault diagnosis and monitoring algorithm combining feature selection and DPCA, which considers the dynamic characteristics of multivariate data and reduces the dimension of the input matrix. At present, the average modeling and diagnostic accuracy of PCA-based fault diagnosis on T2 statistic is 65.49%, and that... [more]

This paper investigates the global fixed-time tracking control problem of nonlinear cascade systems with unknown high powers. In the process of control design, a upper bound and a lower bound of high powers are introduced to compensate the unknown system powers, and a state feedback controller is designed under any initial system conditions. Based on the Lyapunov stability analysis method and the fixed-time stability theory, it is verified that the proposed method can regulate the output tracking error to a disc region of the origin within a fixed-time and all the closed-loop signals are bounded. At last, the effectiveness of the proposed scheme is verified by some simulation results.

This research evaluates the anaerobic digestion (AD) process of the residue generated in a new olive-oil manufacturing process for cold-pressed olive, a residue consisting of a mixture of the wastewater and solid waste obtained from this process. Additionally, in order to assess the possible influence of the level of ripening of the olives on the performance of anaerobic processing, olives of the Picual variety were collected at two stages, i.e., green olives and olives in veraison. The AD processes of the residues obtained from the cold-pressing process and the process without pressure (control) were comparatively assessed by means of biochemical methane potential (BMP) assays conducted at mesophilic temperature (35 ± 1 °C). Maximum values for methane yield (390 ± 1 NL CH4/kg VSadded) and biodegradability (84.5%) were obtained from the cold-pressed green olive residues. For the rest of the wastes studied, biodegradability also reached high values, ranging from 79.1 to 79.6%. The logis... [more]

In this paper, a novel composite fault diagnosis method combining adaptive feature mode decomposition (FMD) and minimum noise amplitude deconvolution (MNAD) is proposed. Firstly, chaos mapping and leader mutation selection strategy were introduced to improve the Northern Goshawk algorithm (NGO), and a chaotic leadership Northern Goshawk optimization (CLNGO) algorithm was proposed. The advantages of the CLNGO algorithm in convergence accuracy and speed were verified by 12 benchmark functions. Then, a new index called sparse pulse and cyclicstationarity (SPC) is proposed to evaluate signal sparsity. Finally, SPC is used as the fitness function of CLNGO to optimize FMD and MNAD. The optimal decomposition mode n and filter length of FMD, and filter length L and noise ratio ρ of MNAD are selected. The CLNGO-FMD is used to decompose signal into different modes. The signal is reconstructed based on the kurtosis criterion and the CLNGO-MNAD method is used to reduce the noise of the reconstruct... [more]

In manufacturing processes using computerized numerical control (CNC) machines, machine tools are operated repeatedly for a long period for machining hard and difficult-to-machine materials, such as stainless steel. These operating conditions frequently result in tool breakage. The failure of machine tools significantly degrades the product quality and efficiency of the target process. To solve these problems, various studies have been conducted for detecting faults in machine tools. However, the most related studies used only the univariate signal obtained from CNC machines. The fault-detection methods using univariate signals have a limitation in that multivariate models cannot be applied. This can restrict in performance improvement of the fault detection. To address this problem, we employed empirical mode decomposition to construct a multivariate dataset from the univariate signal. Subsequently, auto-associative kernel regression was used to detect faults in the machine tool. To v... [more]

Due to the fitted structure of fuzzy logic, neural networks and evolutionary computing, neuro-fuzzy systems, evolutionary-fuzzy systems, and evolutionary neural systems, we can study computational intelligence [...]

In recent times, important contributions and the incorporation of renewable sources, such as photovoltaic, fuel cells, etc., are the main reasons for the popularity of DC microgrids. Integrating renewable sources into the microgrid requires high-voltage, high-efficiency DC-to-DC converters. Without modification, traditional converters are not suitable for achieving the required voltage in microgrid applications, due to the requirement for a large duty cycle, inductor resistance, voltage rating of switches, reverse recovery of the diode, high current rating inductors, etc. Various converters based on the circuitry of voltage multipliers, switched inductors/capacitors, coupled inductors, transformers, etc., have been proposed in the literature and have their drawbacks. In this paper, to realize significant voltage gain with two duty cycle controls, a new multi-leg (ML) converter is recommended as a solution for use in DC microgrids. The converter is designed by incorporating multiple leg... [more]

The anaerobic digestion of swine manure was performed for more than 2 years in a biogas pilot plant with cereal residues as a mono-input, either by a simple intermittent substrate feeding or by feeding with an automated “autopilot” system under the direction of a Fuzzy logic control (FLC) system, working with a closed-loop feedback control. The pilot plant of the University of Applied Sciences in Nordhausen consisted of a 2.5 m3 dosage tank, a 2.5 m3 digestate tank, and a 1 m3 biogas reactor. Only three control parameters were used for FLC: pH, methane %, and the specific gas production rate (GPR) related to the organic loading rate (OLR), that is GPR/OLR m3 biogas/(kgVS d), vs = volatile solids. The specific GPR was referred to the OLR of the last feeding every 8 h in terms of kgVS/(m3 d). In test period I without an FLC system, a safe process with just an OLR of 4 kgVS/(m3 d) was reached, followed by an overloading and reactor disturbance at ≤6.3 kgVS/(m3 d) as indicated by acidifica... [more]

Iron making processes and automation systems are mostly controlled by logical rules and PID controllers. The dynamic behavior of these processes varies due to factors such as raw materials, outdoor conditions, and equipment aging. Changes in system dynamics necessitate re-determination of PID controller parameters. Model reference adaptive controllers (MRACs) are used in many industrial application areas with their adaptability to variable conditions. In this study, an MRAC is applied in the gas cleaning tank system level control problem in the blast furnace facility, which is at the center of the iron making processes. In addition, fuzzy based gain regulation is proposed to improve MRAC performance. MRAC and PID controller system control results are observed and compared. The fast response and adaptation performance of the proposed fuzzy MRAC approach along with external disturbance effects are analyzed. Fuzzy based gain regulation MRAC performances show better performance especially... [more]

The cell wall, serving as the exoskeleton of plants, is naturally a barrier to resist external stresses. Protoplasts can be obtained by dissolving the cell walls of plant cells without damaging the cell membrane, and are widely used in the rapid propagation, transgenic breeding, and somatic hybridization of plants. However, to regenerate the cell wall is a precondition for cell division. Therefore, to study the culture condition and influencing factors during the cell wall regeneration of protoplasts is vital. Traditionally, culture medium is used to cultivate protoplasts, but it has some disadvantages. Herein, a microfluidic system with crossed channels was constructed to isolate and cultivate the protoplasts of tobacco. Then, the cell wall regeneration of the tobacco protoplasts was also studied based on this microfluidic system. It was found that, compared with the control, benzo-(1, 2, 3)-thiadiazole-7-carbothioic acid S-methyl ester (BTH) could accelerate the regeneration of the c... [more]