Advanced Process Control of a cement plant grate cooler

Zanoli, Silvia Maria; Pepe, Crescenzo; Astolfi, Giacomo

doi:10.1109/icstcc55426.2022.9931833

Cited by 5 publications

(5 citation statements)

References 8 publications

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“…An additional field of application of the combination between modeling, simulation, and forecasting is reservoir level control. Different APC techniques can be applied to solve this problem, e.g., MPC [133][134][135][136]. Among the features needed to apply MPC, model and forecasts reliability cover a crucial role.…”

Section: Modelization For Oandmmentioning

confidence: 99%

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Pepe,

Zanoli

2024

Energies

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Intelligent water usage is required in order to target the challenging goals for 2030 and 2050. Hydroelectric power plants represent processes wherein water is exploited as a renewable resource and a source for energy production. Hydroelectric power plants usually include reservoirs, valves, gates, and energy production devices, e.g., turbines. In this context, monitoring and maintenance policies together with control and optimization strategies, at the different levels of the automation hierarchy, may represent strategic tools and drivers for energy efficiency improvement. Nowadays, these strategies rely on different basic concepts and elements, which must be assessed and investigated in order to provide a reliable background. This paper focuses on a review of the state of the art associated with these basic concepts and elements, i.e., digitalization, Industry 4.0, data, KPIs, modelization, and forecast.

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Section: Modelization For Oandmmentioning

confidence: 99%

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Pepe,

Zanoli

2024

Energies

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“…Usually, the MVs are the temperature setpoints related to the level 1 controllers (see Section 2.1.2). MVs are usually characterized by hard constraints on the magnitude and on the allowed variation in the defined sampling time [51,52]. Furthermore, as will be described in Section 2.3, a target can be imposed.…”

Section: Apc Variables and Objectivesmentioning

confidence: 99%

“…The control of ∆T f ,j,P f ,j,d can be performed in two different ways: through a tailored billet temperature difference model or through different management of MVs that separately act on the head and of the tail of the billets (e.g., equalization described in Section 2.1.1). The just described CV types can be characterized by targets and/or soft constraints [51,52]. Other main CVs are represented by the smoke exchanger temperature (see Section 2.1.3) that is upper constrained and by additional variables, e.g., gas valves.…”

Section: Apc Variables and Objectivesmentioning

confidence: 99%

Multi-Mode Model Predictive Control Approach for Steel Billets Reheating Furnaces

Zanoli

Pepe

Orlietti³

2023

Sensors

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In this paper, a unified level 2 Advanced Process Control system for steel billets reheating furnaces is proposed. The system is capable of managing all process conditions that can occur in different types of furnaces, e.g., walking beam and pusher type. A multi-mode Model Predictive Control approach is proposed together with a virtual sensor and a control mode selector. The virtual sensor provides billet tracking, together with updated process and billet information; the control mode selector module defines online the best control mode to be applied. The control mode selector uses a tailored activation matrix and, in each control mode, a different subset of controlled variables and specifications are considered. All furnace conditions (production, planned/unplanned shutdowns/downtimes, and restarts) are managed and optimized. The reliability of the proposed approach is proven by the different installations in various European steel industries. Significant energy efficiency and process control results were obtained after the commissioning of the designed system on the real plants, replacing operators’ manual conduction and/or previous level 2 systems control.

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“…In Section 3, examples of the procedure are discussed. Failure in the N 2 mass flow sensor in the first section (PV1) (3) Error in the control of first stage IGV position (PV2) (4) Error in the horizontal vibration's measurement (PV14) (5) Error in the vertical vibration's measurement (PV15) (6) Fault in the thermocouple relative to the first stage bearing (PV13) (7) Simultaneous fault in the first section N 2 mass flow sensor (PV1) and in the control of first stage IGV position (PV2) (8) Simultaneous fault in the first section N 2 mass flow sensor (PV1) and in the horizontal vibration's measurement (PV14) (9) Simultaneous faults in the first section N 2 mass flow sensor (PV1) and in the vertical vibration's measurement (PV15) (10) Simultaneous faults in the first section N 2 mass flow sensor (PV1) and in the thermocouple relative to the first stage bearing (PV13) (11) Simultaneous faults in the control of first stage IGV position (PV2) and in the horizontal vibration's measurement (PV14) (12) Simultaneous faults in the control of first stage IGV position (PV2) and in the vertical vibration's measurement (PV15) (13) Simultaneous faults in the control of first stage IGV position (PV2) and in the thermocouple relative to the first stage bearing (PV13) (14) Simultaneous error in the vibration's measurements (PV14 and PV15) (15) Simultaneous fault in the horizontal vibration's measurement (PV14) and in the thermocouple relative to the first stage bearing (PV13) (16) Simultaneous fault in the vertical vibration's measurement (PV15) and in the thermocouple relative to the first stage bearing (PV13) (17) Fouling of the first stage of the NMSC (18) Breaking of the thrust bearing relative to the first stage…”

Section: Cluster Analysis Proceduresmentioning

confidence: 99%

“…In the last decades, practitioners, engineers and researchers are focusing their attention on the study, development and implementation of technologies capable to guarantee defined levels of efficiency in industrial and non-industrial processes and machineries [ 1 , 2 , 3 , 4 ]. In this context, Industry 4.0 [ 5 , 6 , 7 ] and Maintenance 4.0 [ 8 , 9 ] are playing a crucial role, together with data selection, storage, and analysis [ 10 , 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery

Zanoli,

Pepe

2023

Sensors

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In the present work, the design and the implementation of a Fault Detection and Isolation (FDI) system for an industrial machinery is proposed. The case study is represented by a multishaft centrifugal compressor used for the syngas manufacturing. The system has been conceived for the monitoring of the faults which may damage the multishaft centrifugal compressor: instrument single and multiple faults have been considered as well as process faults like fouling of the compressor stages and break of the thrust bearing. A new approach that combines Principal Component Analysis (PCA), Cluster Analysis and Pattern Recognition is developed. A novel procedure based on the statistical test ANOVA (ANalysis Of VAriance) is applied to determine the most suitable number of Principal Components (PCs). A key design issue of the proposed fault isolation scheme is the data Cluster Analysis performed to solve the practical issue of the complexity growth experienced when analyzing process faults, which typically involve many variables. In addition, an automatic online Pattern Recognition procedure for finding the most probable faults is proposed. Clustering procedure and Pattern Recognition are implemented within a Fuzzy Faults Classifier module. Experimental results on real plant data illustrate the validity of the approach. The main benefits produced by the FDI system concern the improvement of the maintenance operations, the enhancement of the reliability and availability of the compressor, the increase in the plant safety while achieving reduction in plant functioning costs.

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Advanced Process Control of a cement plant grate cooler

Cited by 5 publications

References 8 publications

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Digitalization, Industry 4.0, Data, KPIs, Modelization and Forecast for Energy Production in Hydroelectric Power Plants: A Review

Multi-Mode Model Predictive Control Approach for Steel Billets Reheating Furnaces

Design and Implementation of a Fuzzy Classifier for FDI Applied to Industrial Machinery

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