Process Disturbances Research Articles

Smith-predictor based enhanced Dual-DOF fractional order control for integrating type CSTRs

Abstract Continuously Stirred Tank Reactors (CSTR) are one of the widely used reactors in the chemical industry. Controlling such reactors is challenging because many times it demonstrates a model which is having a pole at the origin of the s-plane. Moreover, the presence of a dead time necessitates more effective control measures. This work presents a modified smith predictor-based control for integrating type CSTRs with time delay in order to provide adequate servo and regulatory closed-loop responses. Numerous researches on dual DOF control suggested different controller settings for outer and inner-loop controllers. But, in the current study, both the controllers are proposed to be the same which drastically reduces the complexity of the design. To offer good robustness in the closed-loop response, the controller is synthesized with a user-defined maximum sensitivity. Case studies on CSTRs for both the nominal and disturbed process models are conducted and the same is compared with recently developed control laws. Lastly, a performance comparison on ISE, ITAE, and IAE is provided.

Passive Attack Detection for a Class of Stealthy Intermittent Integrity Attacks

This paper proposes a passive methodology for detecting a class of stealthy intermittent integrity attacks in cyber-physical systems subject to process disturbances and measurement noise. A stealthy intermittent integrity attack strategy is first proposed by modifying a zero-dynamics attack model. The stealthiness of the generated attacks is rigorously investigated under the condition that the adversary does not know precisely the system state values. In order to help detect such attacks, a backward-in-time detection residual is proposed based on an equivalent quantity of the system state change, due to the attack, at a time prior to the attack occurrence time. A key characteristic of this residual is that its magnitude increases every time a new attack occurs. To estimate this unknown residual, an optimal fixed-point smoother is proposed by minimizing a piece-wise linear quadratic cost function with a set of specifically designed weighting matrices. The smoother design guarantees robustness with respect to process disturbances and measurement noise, and is also able to maintain sensitivity as time progresses to intermittent integrity attack by resetting the covariance matrix based on the weighting matrices. The adaptive threshold is designed based on the estimated backward-in-time residual, and the attack detectability analysis is rigorously investigated to characterize quantitatively the class of attacks that can be detected by the proposed methodology. Finally, a simulation example is used to demonstrate the effectiveness of the developed methodology.

The Role Of Psychology In Overcoming Student Borneo In Learning

This study aims to see the role of psychology in overcoming student saturation in learning. Learning is an activity carried out by an individual every day, but in learning, various problems are often found in the process of changing behavior. One of them is the feeling of boredom that is experienced when carrying out the learning process. The method used in this research is qualitative research with library research. The results showed that the problem of learning saturation experienced by students was one of the obstacles that made the quality of student learning outcomes low. Factors causing boredom in learning include the lack of motivation in the learning process, the low creativity of the teacher in presenting interesting material in the learning process. So that this does not become a disturbance in the student learning process, the role of psychology is used in overcoming the saturation experienced by students in learning.

Fast DNA-analyses for surveillance of microbial communities in full-scale deammonification tanks: Potential for control and troubleshooting

The partial nitritation/anammox process is a popular process for sidestream nitrogen removal, but the process is sensitive to disturbances and requires extensive surveillance and monitoring for optimal performance. We followed two parallel sidestream full-scale deammonification reactors treating digester centrate for a year with high time-resolution of both online sensor data and microbial community as measured by Nanopore DNA sequencing. DNA surveillance revealed system disturbances and allowed for detection of process and equipment upsets, and it facilitated remediating operational actions. Surveillance of anammox bacteria (Ca. Brocadia) revealed unexpected variations, and the composition and dynamics of the flanking community indicated causes for occasional process disturbances with poor nitrogen removal. Monitoring the ammonia oxidizing bacteria (AOB) and nitrite oxidizing bacteria (NOB) could potentially allow reactor operation with increased dissolved oxygen (DO), yielding higher ammonia conversion while keeping NOB in control. The use of fast and frequent DNA sequencing (sampling 3–5 times a week, analysed once per week) was an important supplement, and in many cases superior, to the online sensor data for process surveillance, understanding and control.

Double Sampling Adaptive Thresholding LASSO Variability Chart for Phase II Monitoring of High-Dimensional Data Streams

In many manufacturing or service systems, the product quality and/or process outcome is described in terms of a large number of quality characteristics. Since the use of large sample sizes in such systems imposes significant costs on the manufacturer, the sample size is usually considered smaller than the process dimension. In this condition, which is referred to high-dimensionality, the sample covariance matrix is not a positive semi-definite matrix. Moreover, another issue to establish variability control charts under high-dimensional setting is their speed to detect the process disturbances under both diagonal and off-diagonal shift patterns. To solve these drawbacks, in this study, a double sampling-based adaptive thresholding LASSO control chart called DSATL is developed to monitor the variability matrix of high-dimensional processes under sparsity assumption. The run length distribution of the DSATL chart under seven out-of-control patterns is evaluated taking a different number of quality characteristics into account. The simulation results show that the detectability of the DSATL chart outperforms the classical adaptive thresholding LASSO one under various changes in the process covariance matrix. The results also confirm that the sensitivity of the DSATL chart for real-time detection of covariance matrix anomalies improves as the process dimension increases.

Optimization-based control strategy for a large-scale polyhydroxyalkanoates production in a fed-batch bioreactor using a coupled PDE–ODE system

Abstract Control strategy development for fed-batch bioreactor (FBBR) plays an important role in the improvement of polyhydroxyalkanoate (PHA) production. To develop a feeding strategy for PHA production in a large-scale FBBR, an optimization-based control scheme that considers nutrient dispersion is proposed in this work. A coupled partial differential equations and ordinary differential equation model is proposed to describe the axial-dispersed nutrient and well-dispersed microbial dynamics with process constraints. An analytical model predictive control (AMPC) method that applies integrated variables of nutrients is employed to develop the real-time control system. The control objective is to regulate the PHA concentration at the updated set points by adjusting the nutrient feed rates; a process disturbance is introduced to evaluate the control robustness. Simulation experiments of a fed-batch operation are conducted to investigate the performance of the developed controller; the controlled output is designed to track the updated set points corresponding to the biomass concentration. Results of closed-loop and regulatory systems showed that the proposed control strategy could provide more productivity (33–38%) compared to the applied PI controller. The performance test demonstrates that the developed control system could apply the biomass concentration for updating set points, provide the optimal control actions that promote PHB accumulation and handle the disturbance effectively.

Insights into the Biocontrol Function of a Burkholderia gladioli Strain against Botrytis cinerea.

Pathogenic fungi are the main cause of yield loss and postharvest loss of crops. In recent years, some antifungal microorganisms have been exploited and applied to prevent and control pathogenic fungi. In this study, an antagonistic bacteria KRS027 isolated from the soil rhizosphere of a healthy cotton plant from an infected field was identified as Burkholderia gladioli by morphological identification, multilocus sequence analysis, and typing (MLSA-MLST) and physiobiochemical examinations. KRS027 showed broad spectrum antifungal activity against various phytopathogenic fungi by secreting soluble and volatile compounds. KRS027 also has the characteristics of plant growth promotion (PGP) including nitrogen fixation, phosphate, and potassium solubilization, production of siderophores, and various enzymes. KRS027 is not only proven safe by inoculation of tobacco leaves and hemolysis test but also could effectively protect tobacco and table grapes against gray mold disease caused by Botrytis cinerea. Furthermore, KRS027 can trigger plant immunity by inducing systemic resistance (ISR) activated by salicylic acid- (SA), jasmonic acid- (JA), and ethylene (ET)-dependent signaling pathways. The extracellular metabolites and volatile organic compounds (VOCs) of KRS027 affected the colony extension and hyphal development by downregulation of melanin biosynthesis and upregulation of vesicle transport, G protein subunit 1, mitochondrial oxidative phosphorylation, disturbance of autophagy process, and degrading the cell wall of B. cinerea. These results demonstrated that B. gladioli KRS027 would likely become a promising biocontrol and biofertilizer agent against fungal diseases, including B. cinerea, and would promote plant growth. IMPORTANCE Searching the economical, eco-friendly and efficient biological control measures is the key to protecting crops from pathogenic fungi. The species of Burkholderia genus are widespread in the natural environment, of which nonpathogenic members have been reported to have great potential for biological control agents and biofertilizers for agricultural application. Burkholderia gladioli strains, however, need more study and application in the control of pathogenic fungi, plant growth promotion, and induced systemic resistance (ISR). In this study, we found that a B. gladioli strain KRS027 has broad spectrum antifungal activity, especially in suppressing the incidence of gray mold disease caused by Botrytis cinerea, and can stimulate plant immunity response via ISR activated by salicylic acid- (SA), jasmonic acid- (JA), and ethylene (ET)-dependent signaling pathways. These results indicate that B. gladioli KRS027 may be a promising biocontrol and biofertilizer microorganism resource in agricultural applications.

Membrane inlet-ion mobility spectrometry with automatic spectra evaluation as online monitoring tool for the process control of microalgae cultivation.

The cultivation of algae either in open raceway ponds or in closed bioreactors could allow the renewable production of biomass for food, pharmaceutical, cosmetic, or chemical industries. Optimal cultivation conditions are however required to ensure that the production of these compounds is both efficient and economical. Therefore, high-frequency analytical measurements are required to allow timely process control and to detect possible disturbances during algae growth. Such analytical methods are only available to a limited extent. Therefore, we introduced a method for monitoring algae release volatile organic compounds (VOCs) in the headspace above a bioreactor in real time. This method is based on ion mobility spectrometry (IMS) in combination with a membrane inlet (MI). The unique feature of IMS is that complete spectra are detected in real time instead of sum signals. These spectral patterns produced in the ion mobility spectrum were evaluated automatically via principal component analysis (PCA). The detected peak patterns are characteristic for the respective algae culture; allow the assignment of the individual growth phases and reflect the influence of experimental parameters. These results allow for the first time a continuous monitoring of the algae cultivation and thus an early detection of possible disturbances in the biotechnological process.

Does population density influence fluctuating asymmetry of Sitophilus oryzae laboratory populations?

The rice weevil, Sitophilus oryzae, is one of the most pernicious pests of stored grain. It is a primary pest and causes a reduction in weight, quality, seed viability and commercial value of various cereals. For this study, we reared S. oryzae on wheat grains under two different adult densities, low and high, with an aim to assess the influence of population density on fluctuating asymmetry of the adult's ventral body. Fluctuating asymmetry represents slight and random deviations from bilateral symmetry normally distributed around a 0 mean, and its level is usually higher under a disturbed developmental process. Accordingly, we expected that environmental stress caused by higher density would increase its level. Opposite to our hypothesis, the study showed that population density did not influence fluctuating asymmetry of S. oryzae adults. Both experimental populations exhibited a similar, non-significant level of fluctuating asymmetry.

Intelligent fault detection algorithm based on Hi/H∞ optimization and a cascaded neural networks

Autonomous surface ships (ASSs) have attracted attention owing to their ability to perform various tasks in complex and challenging aquatic environments without relying on a crew. However, they require reliable sensors to ensure navigational safety. In this study, a robust and intelligent fault detection algorithm was designed for the integrated navigation system of an ASS. First, a residual observer-based fault detection algorithm using Hi/H∞ optimization is proposed to deal with process disturbances and measurement noise. Such noise can be modeled under the condition of a bounded l2-norm to account for the sensitivity and robustness of the residual observer against random noise with unknown properties. However, this fault detection algorithm is insensitive to soft faults, which manifest as noise characterized by a small amplitude and slow variation. Conventional strategies for evaluating the fault detection threshold rely on human experience, which is insufficiently sophisticated for fault detection. Therefore, a cascaded neural network is proposed for optimizing the fault detection algorithm when the amount of training data is limited. The cascaded neural network consists of a multi-feature time domain network, a frequency-domain fault detection network as well as a decision-level fusion network. The proposed algorithm was verified in simulations as well as on historical data collected from real ship sensors. The results demonstrated that the proposed algorithm offers intelligent fault detection, including soft faults, with a low false alarm rate for integrated navigation systems.

Edge computing-based real-time scheduling for digital twin flexible job shop with variable time window

• A new R-DTFJSS idea was developed to realise high-efficiency production. • Edge computing-based R-DTFJSS framework can enhance the data processing ability. • A variable time window-based RS strategy was used to achieve operation allocation. • An IHA-based solve method was designed to reduce the computational complexity. Production scheduling is the central link between enterprise production and operation management and is also the key to realising efficient, high-quality and sustainable production. However, in real-world manufacturing, the frequent occurrence of abnormal disturbance leads to the deviation of scheduling, which affects the accuracy and reliability of scheduling execution. The traditional dynamic scheduling methods (TDSMs) cannot solve this problem effectively. This paper presents a real-time digital twin flexible job shop scheduling (R-DTFJSS) method with edge computing to address the issue. Firstly, an overall framework of R-DTFJSS is proposed to realise real-time scheduling (RS) through real-time interaction between physical workshop (PW) and virtual workshop (VW). Secondly, the implementation process of R-DTFJSS is designed to realise real-time operation allocation. Then, to obtain the optimal RS result, an improved Hungarian algorithm (IHA) is adopted. Finally, a case simulation from an industrial case of a cooperative enterprise is described and analysed to verify the effectiveness of the proposed R-DTFJSS method. The results show that compared with the TDSMs, the R-DTFJSS method can effectively deal with unexpected and frequent abnormal disturbances in the production process.

An In Situ Videotaping Approach for Parameterizing Subsoiling-Induced Soil Disturbance

Subsoiling (SS) is an important technology in conservation tillage, but soil-disturbance characteristics in the SS are rarely described. Research on soil-disturbance characteristics during SS is conducive to the design and optimization of subsoilers, which provides a basis for reducing draft force and energy consumption. This study conducted SS experiments at five different tillage depths in the field with a specific field in situ test-rig facility, and in situ videotaping was made from five positions during SS. The microrelief test, draft force test, disturbance cross-section test and disturbance process analysis were conducted after SS. The results showed that draft force increased with tillage depth as a quadratic function. Soil displacement parameters and soil crack parameters extracted from the video of SS were significantly correlated with tillage depth, which could be used for a quantitative description of the paddy soil-disturbance characteristics. Cross-sectional area showed a trend of “increasing then decreasing” with increasing tillage depth, reaching a maximum at a tillage depth of 20 cm. When the tillage depth was greater than 20 cm, the bottom of the disturbing boundary formed a “mole cavity”. Fallback rate was used to describe the change in disturbed height or width during and after SS, which exceeded 100% at maximum. The surface roughness of microrelief and the size of the average clods reached the maximum at tillage depth of 20 cm. Considering the shallow cultivation layer of paddy soil in rice–wheat rotation, the recommended tillage depth of 20 cm could achieve maximum soil disturbance and minimum energy consumed.

Modeling and identification for practical nonlinear process using neural fuzzy network–based Hammerstein system

To address the strong nonlinearity and unknown disturbance in practical nonlinear process, an identification scheme of neural fuzzy network (NFN)–based Hammerstein nonlinear system using multi-signals is developed in this paper. The proposed Hammerstein system has a static nonlinear subsystem approximated by NFN and a dynamic linear subsystem described by autoregressive exogenous system (ARX). First, the nonlinear subsystem and the linear subsystem are separated and identified by the designed multi-signals, and then parameters of the linear subsystem and noise model are identified simultaneously by making use of recursive extended least squares approach, which is effective for compensating the error caused by output noise. Furthermore, in order to cope with unmeasurable variable issue of the identified system, auxiliary model technology is developed, and the nonlinear subsystem parameters are estimated by applying derived auxiliary model recursive extended least squares approach. Experimental results of three typical nonlinear processes verify the effectiveness and accuracy of the proposed method, and the simulation results show that the proposed method can obtain higher identification accuracy than other optimization algorithms.

A New Decision Method of Flexible Job Shop Rescheduling Based on WOA-SVM

Enterprise production is often interfered with by internal and external factors, resulting in the infeasible original production scheduling scheme. In terms of this issue, it is necessary to quickly decide the optimal production scheduling scheme after these disturbances so that the enterprise is produced efficiently. Therefore, this paper proposes a new rescheduling decision model based on the whale optimization algorithm and support vector machine (WOA-SVM). Firstly, the disturbance in the production process is simulated, and the dimensionality of the data from the simulation is reduced to train the machine learning model. Then, this trained model is combined with the rescheduling schedule to deal with the disturbance in the actual production. The experimental results show that the support vector machine (SVM) performs well in solving classification and decision problems. Moreover, the WOA-SVM can solve problems more quickly and accurately compared to the traditional SVM. The WOA-SVM can predict the flexible job shop rescheduling mode with an accuracy of 89.79%. It has higher stability compared to other machine learning methods. This method can respond to the disturbance in production in time and satisfy the needs of modern enterprises for intelligent production.

Nonlinear full information and moving horizon estimation: Robust global asymptotic stability

In this paper, we propose time-discounted schemes for full information estimation (FIE) and moving horizon estimation (MHE) that are robustly globally asymptotically stable (RGAS). We consider general nonlinear system dynamics with nonlinear process and output disturbances that are a priori unknown. For FIE being RGAS, our only assumptions are that the system is time-discounted incrementally input–output-to-state-stable (i-IOSS) and that the time-discounted FIE cost function is compatible with the i-IOSS estimate. Since for i-IOSS systems such a compatible cost function can always be designed, we show that i-IOSS is sufficient for the existence of RGAS observers. Based on the stability result for FIE, we provide sufficient conditions such that the induced MHE scheme is RGAS as well for sufficiently large horizons. For both schemes, we can guarantee convergence of the estimation error in case the disturbances converge to zero without incorporating a priori knowledge. Finally, we present explicit converge rates and show how to verify that the MHE results approach the FIE results for increasing horizons.

Re-framing deer herbivory as a natural disturbance regime with ecological and socioeconomic outcomes in the eastern United States

Natural disturbances are critical ecosystem processes, with both ecological and socioeconomic benefits and disadvantages. Large herbivores are natural disturbances that have removed plant biomass for millions of years, although herbivore influence likely has declined during the past thousands of years corresponding with extinctions and declines in distributions and abundances of most animal species. Nonetheless, the conventional view, particularly in eastern North America, is that herbivory by large wild herbivores is at unprecedented levels, resulting in unnatural damage to forests. Here, we propose consideration of large herbivores as a natural disturbance that also imparts many crucial ecological advantages, using white-tailed deer (Odocoileus virginianus), the only wild large herbivore remaining throughout the eastern U.S., as our focal species. We examined evidence of detrimental effects of browsing on trees and forbs. We then considered that deer contribute to both fuel reduction and ecological restoration of herbaceous plants and historical open forests of savannas and woodlands by controlling tree and shrub densities, mimicking the consumer role of fire. Similarly to other disturbances, deer disturbance ‘regimes’ are uneven in severity across different ecosystems and landscapes, resulting in heterogeneity and diversity. In addition to biodiversity support and fuel reduction, socioeconomic benefits include >$20 billion dollars per year by 10 million hunters that support jobs and wildlife agencies, non-consumptive enjoyment of nature by 80 million people, cultural importance, and deer as ecological ambassadors, whereas costs include about $5 billion and up to 450 human deaths per year for motor vehicle accidents, along with crop damage and disease transmission. From a perspective of historical ecology rather than current baselines, deer impart a fundamental disturbance process with many ecological benefits and a range of socioeconomic effects.

Hillslope roughness reveals forest sensitivity to extreme winds

Windthrow, or the uprooting of trees by extreme wind gusts, is a natural forest disturbance that creates microhabitats, turns over soil, alters hydrology, and removes carbon from the above-ground carbon stock. Long recurrence intervals between extreme wind events, however, make direct observations of windthrow rare, challenging our understanding of this important disturbance process. To overcome this difficulty, we present an approach that uses the geomorphic record of hillslope topographic roughness as a proxy for the occurrence of windthrow. The approach produces a probability function of the number of annual windthrow events for a maximum wind speed, allowing us to explore how windthrow or tree strengths may change due to shifting wind climates. Slight changes to extreme wind speeds may drive comparatively large changes in windthrow production rates or force trees to respond and change the distribution. We also highlight that topographic roughness has the potential to serve as an important archive of extreme wind speeds.

Multi-Model In-Plant Logistics Using Milkruns for Flexible Assembly Systems under Disturbances: An Industry Study Case

Mass customisation demand requires increasingly flexible assembly operations. For the in-plant logistics of such systems, milkrun trains could present advantages under high variability conditions. This article uses an industrial study case from a global white-goods manufacturing company. A discrete events simulation model was developed to explore the performance of multi-model assembly lines using a set of operational and logistics Key Performance Indicators. Four simulation scenarios analyse the separate effects of an increased number of product models and three different sources of variability. The results show that milkruns can protect the assembly lines from upstream process disturbances.

The Development of an Energy Efficient Temperature Controller for Residential Use and Its Generalization Based on LSTM.

Thermostats operate alongside intelligent home automation systems for ensuring both the comfort of the occupants as well as the responsible use of energy. The effectiveness of such solutions relies on the ability of the adopted control methodology to respond to changes in the surrounding environment. In this regard, process disturbances such as severe wind or fluctuating ambient temperatures must be taken into account. The present paper proposes a new approach for estimating the heat transfer of residential buildings by employing a lumped parameter thermal analysis model. Various control strategies are adopted and tuned into a virtual environment. The knowledge gained is generalized by means of a long short-term memory (LSTM) neural network. Laboratory scale experiments are provided to prove the given concepts. The results achieved highlight the efficiency of the implemented temperature controller in terms of overshoot and energy consumption.

Dynamics and Control of Flexible Satellite Using Reaction Sphere Actuators

This paper presents a disturbance observer-based linear quadratic Gaussian (LQG) control strategy to stabilize the flexible spacecraft considering the vibration suppression of flexible appendages using an orthogonal cluster of magnetically suspended reaction sphere actuators. The nonlinear dynamic equation of a flexible satellite is given and then linearized using the Jacobian method to get a linear state-space model. The dynamic equation of the reaction sphere actuators is derived by considering 2 virtual gimbals. A new steering law is designed to produce the tilt angle commands of orthogonal reaction sphere actuators. The proposed disturbance observer-based LQG considers process disturbances and measurement noises, and performs a trade-off search between control efforts and regulation performance. Numerical simulations are performed to evaluate the proposed strategies for an attitude stabilization scenario, and the results illustrate that the disturbances are effectively mitigated.