scholarly journals Evaluation of a Combined MHE-NMPC Approach to Handle Plant-Model Mismatch in a Rotary Tablet Press

Processes ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1612
Author(s):  
Yan-Shu Huang ◽  
M. Ziyan Sheriff ◽  
Sunidhi Bachawala ◽  
Marcial Gonzalez ◽  
Zoltan K. Nagy ◽  
...  
Keyword(s):  
Product Quality ◽  
Real Time ◽  
Manufacturing Industry ◽  
Time Window ◽  
Mimo Systems ◽  
Mechanistic Model ◽  
Model Parameters ◽  
Plant Model ◽  
Model Mismatch ◽  
Highly Sensitive

The transition from batch to continuous processes in the pharmaceutical industry has been driven by the potential improvement in process controllability, product quality homogeneity, and reduction of material inventory. A quality-by-control (QbC) approach has been implemented in a variety of pharmaceutical product manufacturing modalities to increase product quality through a three-level hierarchical control structure. In the implementation of the QbC approach it is common practice to simplify control algorithms by utilizing linearized models with constant model parameters. Nonlinear model predictive control (NMPC) can effectively deliver control functionality for highly sensitive variations and nonlinear multiple-input-multiple-output (MIMO) systems, which is essential for the highly regulated pharmaceutical manufacturing industry. This work focuses on developing and implementing NMPC in continuous manufacturing of solid dosage forms. To mitigate control degradation caused by plant-model mismatch, careful monitoring and continuous improvement strategies are studied. When moving horizon estimation (MHE) is integrated with NMPC, historical data in the past time window together with real-time data from the sensor network enable state estimation and accurate tracking of the highly sensitive model parameters. The adaptive model used in the NMPC strategy can compensate for process uncertainties, further reducing plant-model mismatch effects. The nonlinear mechanistic model used in both MHE and NMPC can predict the essential but complex powder properties and provide physical interpretation of abnormal events. The adaptive NMPC implementation and its real-time control performance analysis and practical applicability are demonstrated through a series of illustrative examples that highlight the effectiveness of the proposed approach for different scenarios of plant-model mismatch, while also incorporating glidant effects.

Predicting orbit and clock corrections during their outage in real-time positioning using GPS, GLONASS and QZSS for natural hazard warning systems

2019 ◽  
Vol 13 (2) ◽  
pp. 69-79
Author(s):  
Ahmed El-Mowafy
Keyword(s):  
Real Time ◽  
Natural Hazard ◽  
Time Window ◽  
Model Parameters ◽  
Prediction Errors ◽  
Warning Systems ◽  
International Gnss Service ◽  
Positioning Method ◽  
Clock Analysis ◽  
Hazard Warning

Abstract Real-time Precise Point Positioning (RT PPP) is a primary positioning method used in natural hazard warning systems (NHWS) such as monitoring tsunami and earthquakes. The method relays on precise orbit and clock corrections to eliminate satellite-related errors and its performance can be significantly improved by using measurements from multi-GNSS constellations compared with using only one system, such as GPS. The Japanese Multi-GNSS Advanced Demonstration tool for Orbit and Clock Analysis (MADOCA) provides these corrections for GPS, GLONASS and QZSS satellites enabling a multi-GNSS RT-PPP. However, the accuracy of RT PPP will suffer a major decline in case of presence of an outage in receiving these corrections, for instance due to a temporary failure of the user modem. For that reason, a method is proposed to maintain RT PPP when such a break takes place. For short outages less than 30 minutes we predict MADOCA orbits using a Holt-Winters’ auto-regressive model, and for longer outages up to 1 hr, the most recent International GNSS Service (IGS) ultra-rapid orbits can be used, but only for GPS. In addition, the clock corrections are predicted as a time series using a linear model with sinusoidal terms. The best regression period to estimate the required model parameters is discussed based on analysis of the autocorrelation of the corrections. The prediction model parameters are estimated using a sliding time window. Evaluation of the proposed method showed that positioning accuracy of 15 cm was maintained during the prediction period, which is twice better than using IGS ultra-rapid predicted products. For NHSW, the displacement errors due to prediction errors were generally within ±6 cm with one min interval and ±10 cm with five min interval.

A Highly Efficient and Optimised Simulation Based Multi objective Decision-Making for FMS Control

2018 ◽  
Vol 6 (6) ◽  
pp. 98
Author(s):  
Shreyanshu Parhi ◽  
S. C. Srivastava
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Manufacturing Industry ◽  
Simulation Software ◽  
Shop Floor ◽  
Simulation Tool ◽  
Routing Flexibility ◽  
Decision System ◽  
Multi Objective ◽  
Arena Simulation

Optimized and efficient decision-making systems is the burning topic of research in modern manufacturing industry. The aforesaid statement is validated by the fact that the limitations of traditional decision-making system compresses the length and breadth of multi-objective decision-system application in FMS.  The bright area of FMS with more complexity in control and reduced simpler configuration plays a vital role in decision-making domain. The decision-making process consists of various activities such as collection of data from shop floor; appealing the decision-making activity; evaluation of alternatives and finally execution of best decisions. While studying and identifying a suitable decision-making approach the key critical factors such as decision automation levels, routing flexibility levels and control strategies are also considered. This paper investigates the cordial relation between the system ideality and process response time with various prospective of decision-making approaches responsible for shop-floor control of FMS. These cases are implemented to a real-time FMS problem and it is solved using ARENA simulation tool. ARENA is a simulation software that is used to calculate the industrial problems by creating a virtual shop floor environment. This proposed topology is being validated in real time solution of FMS problems with and without implementation of decision system in ARENA simulation tool. The real-time FMS problem is considered under the case of full routing flexibility. Finally, the comparative analysis of the results is done graphically and conclusion is drawn.

Modelling heavy metal mobilisation in solid waste deposits - a predictive tool for environmental risk assessment

1999 ◽  
Vol 39 (10-11) ◽  
pp. 193-196
Author(s):  
J. Petersen ◽  
J. G. Petrie
Keyword(s):  
Heavy Metal ◽  
Solid Waste ◽  
Environmental Risk ◽  
Mechanistic Model ◽  
Small Sample ◽  
Waste Material ◽  
Metal Species ◽  
Model Parameters ◽  
Assessment Methodology ◽  
Predictive Tool

The release of heavy metal species from deposits of solid waste materials originating from minerals processing operations poses a serious environmental risk should such species migrate beyond the boundaries of the deposit into the surrounding environment. Legislation increasingly places the liability for wastes with the operators of the process that generates them. The costs for long-term monitoring and clean-up following a potential critical leakage have to be factored in the overall project plan from the outset. Thus assessment of the potential for a particular waste material to generate a harmful leachate is directly relevant for estimating the environmental risk associated with the planned disposal operation. A rigorous mechanistic model is proposed, which allows prediction of the time-dependent generation of a leachate from a solid mineral waste deposit. Model parameters are obtained from a suitably designed laboratory waste assessment methodology on a relatively small sample of the prospective waste material. The parameters are not specific to the laboratory environment in which they were obtained but are valid also for full-scale heap modelling. In this way the model, combined with the assessment methodology, becomes a powerful tool for meaningful assessment of the risks associated with solid waste disposal strategies.

scholarly journals Real-time automatic uncertainty estimation of coseismic single rectangular fault model using GNSS data

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Keitaro Ohno ◽  
Yusaku Ohta ◽  
Satoshi Kawamoto ◽  
Satoshi Abe ◽  
Ryota Hino ◽  
...  
Keyword(s):  
Real Time ◽  
Disaster Response ◽  
Order Approximation ◽  
Nonlinear Problems ◽  
Large Earthquake ◽  
Fault Model ◽  
Deformation Monitoring ◽  
Model Parameters ◽  
The Sea Of Japan ◽  
Plate Interface

AbstractRapid estimation of the coseismic fault model for medium-to-large-sized earthquakes is key for disaster response. To estimate the coseismic fault model for large earthquakes, the Geospatial Information Authority of Japan and Tohoku University have jointly developed a real-time GEONET analysis system for rapid deformation monitoring (REGARD). REGARD can estimate the single rectangular fault model and slip distribution along the assumed plate interface. The single rectangular fault model is useful as a first-order approximation of a medium-to-large earthquake. However, in its estimation, it is difficult to obtain accurate results for model parameters due to the strong effect of initial values. To solve this problem, this study proposes a new method to estimate the coseismic fault model and model uncertainties in real time based on the Bayesian inversion approach using the Markov Chain Monte Carlo (MCMC) method. The MCMC approach is computationally expensive and hyperparameters should be defined in advance via trial and error. The sampling efficiency was improved using a parallel tempering method, and an automatic definition method for hyperparameters was developed for real-time use. The calculation time was within 30 s for 1 × 106 samples using a typical single LINUX server, which can implement real-time analysis, similar to REGARD. The reliability of the developed method was evaluated using data from recent earthquakes (2016 Kumamoto and 2019 Yamagata-Oki earthquakes). Simulations of the earthquakes in the Sea of Japan were also conducted exhaustively. The results showed an advantage over the maximum likelihood approach with a priori information, which has initial value dependence in nonlinear problems. In terms of application to data with a small signal-to-noise ratio, the results suggest the possibility of using several conjugate fault models. There is a tradeoff between the fault area and slip amount, especially for offshore earthquakes, which means that quantification of the uncertainty enables us to evaluate the reliability of the fault model estimation results in real time.

scholarly journals Data-Driven Dispatching Rules Mining and Real-Time Decision-Making Methodology in Intelligent Manufacturing Shop Floor with Uncertainty

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4836
Author(s):  
Liping Zhang ◽  
Yifan Hu ◽  
Qiuhua Tang ◽  
Jie Li ◽  
Zhixiong Li
Keyword(s):  
Decision Making ◽  
Data Base ◽  
Real Time ◽  
Manufacturing Industry ◽  
Job Shop ◽  
Data Driven ◽  
Dispatching Rules ◽  
Shop Floor ◽  
Production Data ◽  
Online Decision Making

In modern manufacturing industry, the methods supporting real-time decision-making are the urgent requirement to response the uncertainty and complexity in intelligent production process. In this paper, a novel closed-loop scheduling framework is proposed to achieve real-time decision making by calling the appropriate data-driven dispatching rules at each rescheduling point. This framework contains four parts: offline training, online decision-making, data base and rules base. In the offline training part, the potential and appropriate dispatching rules with managers’ expectations are explored successfully by an improved gene expression program (IGEP) from the historical production data, not just the available or predictable information of the shop floor. In the online decision-making part, the intelligent shop floor will implement the scheduling scheme which is scheduled by the appropriate dispatching rules from rules base and store the production data into the data base. This approach is evaluated in a scenario of the intelligent job shop with random jobs arrival. Numerical experiments demonstrate that the proposed method outperformed the existing well-known single and combination dispatching rules or the discovered dispatching rules via metaheuristic algorithm in term of makespan, total flow time and tardiness.

Highly Sensitive Detection of Paraquat with Pillar[5]arene as an aptamer in α-Hemolysin Nanopore

2021 ◽  
Author(s):  
Xiaojia Jiang ◽  
Mingsong Zang ◽  
Fei Li ◽  
Chunxi Hou ◽  
Quan Luo ◽  
...  
Keyword(s):  
Real Time ◽  
High Throughput ◽  
Single Molecule ◽  
Single Molecule Detection ◽  
Sensitive Detection ◽  
High Throughput Analysis ◽  
Throughput Analysis ◽  
The Real ◽  
Highly Sensitive ◽  
Highly Sensitive Detection

Biological nanopore-based techniques have attracted more and more attention recently in the field of single-molecule detection, because they allow the real-time, sensitive, high-throughput analysis. Herein, we report an engineered biological...

Detection by real-time quaking-induced conversion (RT-QuIC), ELISA, and IHC of chronic wasting disease prion in lymph nodes from Pennsylvania white-tailed deer with specific PRNP genotypes

2021 ◽  
pp. 104063872110214
Author(s):  
Deepanker Tewari ◽  
David Steward ◽  
Melinda Fasnacht ◽  
Julia Livengood
Keyword(s):  
Real Time ◽  
Disease Susceptibility ◽  
Chronic Wasting Disease ◽  
Low Frequency ◽  
The United States ◽  
Detection Methods ◽  
Spongiform Encephalopathy ◽  
Wasting Disease ◽  
Diagnostic Laboratory ◽  
Highly Sensitive

Chronic wasting disease (CWD) is a prion-mediated, transmissible disease of cervids, including deer ( Odocoileus spp.), which is characterized by spongiform encephalopathy and death of the prion-infected animals. Official surveillance in the United States using immunohistochemistry (IHC) and ELISA entails the laborious collection of lymphoid and/or brainstem tissue after death. New, highly sensitive prion detection methods, such as real-time quaking-induced conversion (RT-QuIC), have shown promise in detecting abnormal prions from both antemortem and postmortem specimens. We compared RT-QuIC with ELISA and IHC for CWD detection utilizing deer retropharyngeal lymph node (RLN) tissues in a diagnostic laboratory setting. The RLNs were collected postmortem from hunter-harvested animals. RT-QuIC showed 100% sensitivity and specificity for 50 deer RLN (35 positive by both IHC and ELISA, 15 negative) included in our study. All deer were also genotyped for PRNP polymorphism. Most deer were homozygous at codons 95, 96, 116, and 226 (QQ/GG/AA/QQ genotype, with frequency 0.86), which are the codons implicated in disease susceptibility. Heterozygosity was noticed in Pennsylvania deer, albeit at a very low frequency, for codons 95GS (0.06) and 96QH (0.08), but deer with these genotypes were still found to be CWD prion-infected.

scholarly journals Modeling Bacterial Regrowth and Trihalomethane Formation in Water Distribution Systems

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 463
Author(s):  
Gopinathan R. Abhijith ◽  
Leonid Kadinski ◽  
Avi Ostfeld
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Mechanistic Model ◽  
Water Distribution Systems ◽  
Sensitivity Study ◽  
Operating Conditions ◽  
Growth Rate Constant ◽  
Model Parameters ◽  
Bacterial Regrowth ◽  
The Impact

The formation of bacterial regrowth and disinfection by-products is ubiquitous in chlorinated water distribution systems (WDSs) operated with organic loads. A generic, easy-to-use mechanistic model describing the fundamental processes governing the interrelationship between chlorine, total organic carbon (TOC), and bacteria to analyze the spatiotemporal water quality variations in WDSs was developed using EPANET-MSX. The representation of multispecies reactions was simplified to minimize the interdependent model parameters. The physicochemical/biological processes that cannot be experimentally determined were neglected. The effects of source water characteristics and water residence time on controlling bacterial regrowth and Trihalomethane (THM) formation in two well-tested systems under chlorinated and non-chlorinated conditions were analyzed by applying the model. The results established that a 100% increase in the free chlorine concentration and a 50% reduction in the TOC at the source effectuated a 5.87 log scale decrement in the bacteriological activity at the expense of a 60% increase in THM formation. The sensitivity study showed the impact of the operating conditions and the network characteristics in determining parameter sensitivities to model outputs. The maximum specific growth rate constant for bulk phase bacteria was found to be the most sensitive parameter to the predicted bacterial regrowth.

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