Multicriteria Ammonia Plant Assessment for the Advanced Process Control Implementation

Dziuba, Krzysztof; Góra, R.; Domański, Paweł D.; Ławryńczuk, Maciej

doi:10.1109/access.2020.3038206

Cited by 5 publications

(5 citation statements)

References 35 publications

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“…The utilization of robust statistics is just straightforward, as they form the natural extension of the statistical scale measures (variance and standard deviation) in case o outliers [25], which occur frequently in real-life applications [26]. With their use, we are not biasing our assessment by anomalies or erroneous records.…”

Section: Robust Statisticsmentioning

confidence: 99%

Multicriteria Machine Learning Model Assessment—Residuum Analysis Review

Kaniuka,

Ostrysz,

Groszyk

et al. 2024

Electronics

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The use of machine learning (ML) and its applications is one of the leading research areas nowadays. Neural networks have recently gained enormous popularity and many works in various fields use them in the hope of improving previous results. The application of the artificial intelligence (AI) methods and the rationale for this decision is one issue, but the assessment of such a model is a completely different matter. People mostly use mean square error or less often mean absolute error in the absolute or percentage versions. One should remember that an error does not equal an error and a single value does not provide enough knowledge about the causes of some behavior. Proper interpretation of the results is crucial. It leads to further model improvement. It might be challenging, but allows us to obtain better and more robust solutions, which ultimately solve real-life problems. The ML model assessment is the multicriteria task. A single measure delivers only a fraction of the picture. This paper aims at filling that research gap. Commonly used integral measures are compared with alternative measures like factors of the Gaussian and non-Gaussian statistics, robust statistical estimators, tail index and the fractional order. The proposed methodology delivers new single-criteria indexes or the multicriteria approach, which extend the statistical concept of the moment ratio diagram (MRD) into the index ratio diagram (IRD). The proposed approach is validated using real data from the Full Truck Load cost estimation example. It compares 35 different ML regression algorithms applied to that task. The analysis gives an insight into the properties of the selected methods, enables their comparison and homogeneity analysis and ultimately leads towards constructive suggestions for their eventual proper use. The paper proposes new indexes and concludes that correct selection of the residuum analysis methodology makes the assessment and the ML regression credible.

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Section: Robust Statisticsmentioning

confidence: 99%

Multicriteria Machine Learning Model Assessment—Residuum Analysis Review

Kaniuka,

Ostrysz,

Groszyk

et al. 2024

Electronics

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“…The plant is equipped with a modern control system that uses a hierarchical advanced process control strategy with a basic layer utilizing PID control algorithms, which are supervised using model predictive control [46]. All PID loops are well-tuned and maintained.…”

Section: Industrial Validationmentioning

confidence: 99%

PID Control Assessment Using L-Moment Ratio Diagrams

Domański,

Dziuba,

Góra

2024

Applied Sciences

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This paper presents an application of L-moments and respective L-moment ratio diagrams (LMRD) to the task of control performance assessment (CPA). An L-moment ratio diagram is a graphical approach to the visualization of statistical properties for a given time series. Moreover, it enables comparing various data, showing their similarities and homogeneity. Simultaneously, CPA aims at measuring the control loop quality, supporting decision-making about their tuning and maintenance. This paper shows that control system quality can be efficiently visualized using LMRDs. The method was analyzed using simulations and further validated at a real chemical engineering industrial ammonia synthesis plant.

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“…Control performance depends on the quality of the used Instrumentation and Control (I&C) infrastructure (i.e. sensors, control system hardware and actuators) using the Distributed Control System (DCS) originally designed for process plants (Araujo & Skogestad, 2008;Borikar, 2018;Dziuba et al, 2020). The whole process I&C diagram is as shown in Figure 8.…”

Section: Process Controlmentioning

confidence: 99%

Process Modeling and Simulation of Ammonia Production from Natural Gas: Control and Response Analysis

Abubakar

Royani

Gezerman

et al. 2023

JTIE

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Optimal production of ammonia (NH3) using natural gas is necessary in order to make it available for wide range of applications including the manufacture of fertilizers, fuel for transportation and during synthesis of some chemicals. Achieving this would require strategic implementation of a control scheme to simulated ammonia production, capable of ensuring adequate realization of production targets. The work involves ASPEN Plus modeling, simulation, sensitivity analysis and control of NH3 production process. Steam/carbon ratio, conversion of CH4, removal of carbon dioxide (CO2) and carbon monoxide (CO), hydrogen/nitrogen ratio and heat exchanger and separator temperatures were identified as requiring control in units any of these specifically impacts. As a result, approximately 176 tons of NH3 was realized daily based on the simulation results and can be scaled-up using a calculated factor equivalent to 1.1375 to 200 tons/day capacity, in this design. Sensitivity analysis resulting in control of certain unit parameters is effective in ensuring process safety, maximum yield of important end-products and reduction in the cost of operation

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Multicriteria Ammonia Plant Assessment for the Advanced Process Control Implementation

Cited by 5 publications

References 35 publications

Multicriteria Machine Learning Model Assessment—Residuum Analysis Review

Multicriteria Machine Learning Model Assessment—Residuum Analysis Review

PID Control Assessment Using L-Moment Ratio Diagrams

Process Modeling and Simulation of Ammonia Production from Natural Gas: Control and Response Analysis

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