The Experts below are selected from a list of 44781 Experts worldwide ranked by ideXlab platform
C C Chiu - One of the best experts on this subject based on the ideXlab platform.
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integrated application of spc epc ica and neural networks
2008Co-Authors: Chijie Lu, Ching-jui Keng, C.m. Wu, C C ChiuAbstract:There are many studies that have been conducted about the integrated use of statistical Process control (SPC) and engineering Process control (EPC) because using them individually cannot optimally control the manufacturing Process. The majority of these studies have reported that the integrated approach has better performance than using only SPC or EPC. Among all these studies, most of them have assumed that the assignable causes of Process Disturbance can be effectively identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. The paper discusses the development of neural network models with independent component analysis (ICA) to identify the Disturbance and recognize shifts in the correlated Process parameters. Moreover, these designed network models can be used to monitor and eliminate manufacturing Process parameters when Disturbance happens in the underlying Process. For comparison, the traditional Shewhar...
Chijie Lu - One of the best experts on this subject based on the ideXlab platform.
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integrated application of spc epc ica and neural networks
2008Co-Authors: Chijie Lu, Ching-jui Keng, C.m. Wu, C C ChiuAbstract:There are many studies that have been conducted about the integrated use of statistical Process control (SPC) and engineering Process control (EPC) because using them individually cannot optimally control the manufacturing Process. The majority of these studies have reported that the integrated approach has better performance than using only SPC or EPC. Among all these studies, most of them have assumed that the assignable causes of Process Disturbance can be effectively identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. The paper discusses the development of neural network models with independent component analysis (ICA) to identify the Disturbance and recognize shifts in the correlated Process parameters. Moreover, these designed network models can be used to monitor and eliminate manufacturing Process parameters when Disturbance happens in the underlying Process. For comparison, the traditional Shewhar...
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Process Disturbance identification using ica based image reconstruction scheme with neural network
2007Co-Authors: Shienping Huang, Chihchou Chiu, D F Cook, Chijie LuAbstract:Process monitoring and control of a production line are often used in industry to maintain high-quality production and to facilitate high levels of efficiency in the Process. However, current Process control techniques, such as statistical Process control (SPC) and engineering Process control (EPC), may not effectively detect abnormalities, especially when autocorrelation is present in the Process. This paper proposes an independent component analysis (ICA)-based image reconstruction scheme with a neural network approach to identify Disturbances and recognize shifts in the correlated Process parameters. The resulting image can effectively remove the textual pattern and preserve Disturbances distinctly. We illustrate our approach using two most commonly encountered Disturbances, the step-change Disturbance and the linear Disturbance, in a manufacturing Process. The experimental results reveal that the proposed method is effective and efficient for Disturbance identification in correlated Process parameters when Disturbance is significant. Additionally, the identification rate made by the proposed method is slightly influenced by the data correlation.
Susanne Theuerl - One of the best experts on this subject based on the ideXlab platform.
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nexus between the microbial diversity level and the stress tolerance within the biogas Process
2019Co-Authors: Johanna Klang, Ulrich Szewzyk, Daniel Bock, Susanne TheuerlAbstract:To investigate whether there is a nexus between the microbial diversity level (taxonomic, functional and ecological) and the stress tolerance potential of the microbial community, the impact of different ammonium sources was evaluated. Therefore reactors adapted either to the anaerobic digestions of sugar beet silage or maize silage (SBS/MS) were supplemented with animal manure (M) or ammonium carbonate (A). The results showed that increasing concentrations of total ammonium nitrogen (TAN) were not the only reason for community changes: the bacterial community in the reactors given animal manure became more similar over time compared to the reactors given ammonium carbonate. However, this study revealed that a bacterial community with a few dominant members led to a functional more flexible archaeal community (SBS reactors) which was more stress resistant under the experimental conditions. This indicates that a higher functional diversity within a certain part of the community, in the present study the archaeal community, is one important factor for Process stability due to a higher tolerance to increasing amounts of Process-inhibiting metabolites such as TAN. Compared to this a bacterial community with higher amount of more evenly distributed community members combined with a more rigid archaeal community (MS reactors) showed a lower stress tolerance potential. Moreover it was observed that the disappearance of members of the phylum Cloacimonetes can be used as an indicator for an upcoming Process Disturbance due to increasing TAN concentrations.
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reorganisation of a mesophilic biogas microbiome as response to a stepwise increase of ammonium nitrogen induced by poultry manure supply
2016Co-Authors: Khulud Alsouleman, Bernd Linke, Johanna Klang, Michael Klocke, Niclas Krakat, Susanne TheuerlAbstract:An anaerobic digestion experiment was investigated to evaluate the impact of increasing amounts of ammonium nitrogen due to poultry manure addition on the reactor performance, especially on the microbiome response. The microbial community structure was assessed by using a 16S rRNA gene approach, which was further correlated with the prevalent environmental conditions by using statistical analyses. The addition of 50% poultry manure led to a Process Disturbance indicated by a high VFA content (almost 10 g(HAc-Eq) L(-1)) in combination with elevated concentrations of ammonium nitrogen (5.9 g NH4(+)-N kg(FM)(-1)) and free ammonia (0.5 g NH3 kg(FM)(-1)). Simultaneously the microbiome, changed from a Bacteroidetes-dominated to a Clostridiales-dominated community accompanied by a shift from the acetoclastic to the hydrogenotrophic pathway. The "new" microbial community was functional redundant as the overall Process rates were similar to the former one. A further increase of poultry manure resulted in a complete Process failure.
Hua Deng - One of the best experts on this subject based on the ideXlab platform.
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effective tuning method for fuzzy pid with internal model control
2008Co-Authors: Xiaogang Duan, Hanxiong Li, Hua DengAbstract:An internal model control (IMC) based tuning method is proposed to autotune the fuzzy proportional integral derivative (PID) controller in this paper. An analytical model of the fuzzy PID controller is first derived, which consists of a linear PID controller and a nonlinear compensation item. The nonlinear compensation item can be considered as a Process Disturbance, and then parameters of the fuzzy PID controller can be analytically determined on the basis of the IMC structure. The stability of the fuzzy PID control system is analyzed using the Lyapunov stability theory. The simulation results demonstrate the effectiveness of the proposed tuning method.
Ching-jui Keng - One of the best experts on this subject based on the ideXlab platform.
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integrated application of spc epc ica and neural networks
2008Co-Authors: Chijie Lu, Ching-jui Keng, C.m. Wu, C C ChiuAbstract:There are many studies that have been conducted about the integrated use of statistical Process control (SPC) and engineering Process control (EPC) because using them individually cannot optimally control the manufacturing Process. The majority of these studies have reported that the integrated approach has better performance than using only SPC or EPC. Among all these studies, most of them have assumed that the assignable causes of Process Disturbance can be effectively identified and removed by SPC techniques. However, these techniques are typically time-consuming and thus make the search hard to implement in practice. The paper discusses the development of neural network models with independent component analysis (ICA) to identify the Disturbance and recognize shifts in the correlated Process parameters. Moreover, these designed network models can be used to monitor and eliminate manufacturing Process parameters when Disturbance happens in the underlying Process. For comparison, the traditional Shewhar...