Development of control system for a complex technological object using fuzzy behavior charts

Bazhanov, Alexander; Vashchenko, Roman; Rubanov, V. G.

doi:10.1016/j.heliyon.2020.e03393

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…The complex dynamics and multi-variable nature of the calcination process, make the kiln process inherently difficult to operate efficiently. Bazhanov et al [6] presented an approach to dynamic processes modeling based on second rank fuzzy behavior charts. The approach is used in control system development method during an advising control system construction for a wet method rotating cement kiln.…”

Section: Related Workmentioning

confidence: 99%

“…To stabilize some key controlled variables, several advanced control strategies including fuzzy control, [2,[4][5][6] intelligent control, [7][8][9][10] intelligent control system based on human-machine interaction, [11][12][13][14] and predictive control [1,11,15] have been introduced into process control of rotary kilns. But these strategies are valid where boundary conditions do not change frequently.…”

Section: Related Workmentioning

confidence: 99%

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A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Ali

Tabares

McGinley

2022

J Adv Manuf & Process

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Cement manufacturing is energy‐intensive (5Gj/t) and comprises a significant portion of the energy footprint of concrete systems. Incorporating modern monitoring, simulation and control systems will allow lower energy use, lower environmental impact, and lower costs of this widely used construction material. One of the goals of the CESMII roadmap project on the Smart Manufacturing of Cement included developing an analytical process model for clinker quality that includes the chemistry of the kiln feed and accounts for critical process variables. This predictive model will be used in nonlinear model predictive control system designed to significantly reduce process energy use while maintaining or improving product quality. In the cement manufacturing plant used in this study, the kiln feed (meal) is tested every 12 h and used to estimate the mineral composition of the cement kiln output (clinker) using the stoichiometry‐based Bogue's model and the expertise of the plant operators. During kiln operation, kiln output (clinker) is sampled and tested every 2 h to measure its chemical and mineral composition. The predicted and measured values of the clinker composition are used by the plant operators to adjust the kiln input stream and the production process characteristics to maintain stable operation and uniform product quality. However, the time delay between prediction and testing, along with inaccuracies inherent in the Bogue's model have made any process changes designed to minimize energy use problematic, especially in‐light of potential clinker quality issues that process changes often pose. A new analytical model that integrates quality information and process operation information has been developed from data collected from 2 years of production from an operating cement facility. To make the model fuel‐type‐independent, consumed heat energy was computed in the model instead of fuel type and amount. A Feedforward Network was trained and tailored from collected data. Many data‐based simulations were conducted to quantitatively evaluate the proposed model and the 5‐fold cross‐validation procedure was used to test the models. The resulting predictive model was shown to have a low root mean square error (MSE) with respect to the estimated clinker mineral composition compared to that using the industry standard “Bogue’ model”. The end goal of this work was to develop a single machine learning tool that allows the use of quality control data and process control variables to improve energy efficiency of the process in a continuous fashion. The proposed nonlinear model predictive control system (NMPC) can generate predicted kiln production characteristics based on manipulated variables in manner that accurately follows the target product quality values. Simulation results also show that the proposed model produced accurate predictions of kiln outputs that fell within the required constraints, while manipulating control variables within typical operational ranges.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Ali

Tabares

McGinley

2022

J Adv Manuf & Process

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“…This is an important point for the success of this study in reconstructing Songket motifs systematically. Without changing the meaning associated with customary norms and rules, Songket can be used as guidelines in life because the motif is very complex in the object [8]- [10]. Therefore, it is necessary to develop the value of the Affine transformation matrix in which the Affine transformation has definitely symmetric positive manifold matrix value [11]- [13].…”

Section: Introductionmentioning

confidence: 99%

Development of Affine Transformation Method in the Reconstruction of Songket Motif

Ramadhanu¹,

Na’am²,

Nurcahyo³

et al. 2022

International Journal on Advanced Science, Engineering and Information Technology

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Songket is one of the clothes with a high artistic value besides Batik originating from Indonesia. Songket is widely used at weddings and other traditional formal events. Songket is the result of a traditional craft consisting of the main ingredients of cloth made of cotton and silk threads by inserting the feed (basic material) transversely on the Lunsing (yeast cloth). The quality of Songket art is found in the motifs on the Songket. This research has a conceptual outline in terms of improving the quality of Songket cloth and making it easier for craftsmen and artists to spare their art for the reconstruction process of the Silungkang Songket motif. This research aims to get a new Songket motif with a more modern and aesthetic motif but still has its original characteristics and identity. Based on these objectives, the researchers focused and motivated to develop the motif of one of the types of Songket in Indonesia, namely Silungkang Songket, in the form of a more modernized motif. This research develops a digital image transformation method named Affine Segmentation Point (ASP) to produce modernized Songket motifs. The basis of the developed ASP method is the Affine transformation. Songket image input was used as data and created a new motif in this study: a Songket image with the Rangkiang Lumbung Padi Besar motif. The results of this study can create excellent Songket motifs and modernization. The new motifs produced have been tested by Songket experts, Songket observers, Songket craftsmen, and Songket sellers with an interest rate of 97.5%. The method developed is very precise and can be used to develop other high-value artistic motifs on traditional cloth.

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“…On the other hand, attribute control charts must be used if the quality characteristic cannot be easily represented in numerical form. However, for at least three decades, trends in research have dealt with the issue of control charts based on the fuzzy set theory [8][9][10][11], and this approach is still widely used [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE

et al. 2022

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The capability analysis of a process against requirements is often an instrument of change. The traditional and fuzzy process capability approaches are the most useful statistical techniques for determining the intrinsic spread of a controlled process for establishing realistic specifications and use for comparative processes. In the industry, the traditional approach is the most commonly used instrument to assess the impact of continuous improvement projects. However, these methods used to evaluate process capability indices could give misleading results because the dataset employed corresponds to the final product/service measures. This paper reviews an alternative procedure to assess the fuzzy process capability indices based on the statistical methodology involved in the modeling and design of experiments. Firstly, a model with reasonable accuracy is developed using a neural network approach. This model is embedded in a graphic user interface (GUI). Using the GUI, an experimental design is carried out, first to know the membership function of the process variability and then include this variability in the model. Again, an experimental design identifies the improved operating conditions for the significative independent variables. A new dataset is generated with these operating conditions, including the minimum error reached for each independent variable. Finally, the GUI is used to get a new prediction for the response variable. The fuzzy process capability indices are determined using the triangular membership function and the predicted response values. The feasibility of the proposed method was validated using a random data set corresponding to the basis weight of a papermaking process. The results indicate that the proposed method provides a better overview of the process performance, showing its true potential. The proposed method can be considered non-invasive.

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Development of control system for a complex technological object using fuzzy behavior charts

Cited by 4 publications

References 25 publications

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

A machine learning approach for clinker quality prediction and nonlinear model predictive control design for a rotary cement kiln

Development of Affine Transformation Method in the Reconstruction of Songket Motif

A Non-Invasive Method to Evaluate Fuzzy Process Capability Indices via Coupled Applications of Artificial Neural Networks and the Placket–Burman DOE

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