Premi e Riconoscimenti

Nel 2014 i.Process ha progettato e avviato il primo sistema APC basato su Adaptive Model Predictive Control – novità assoluta per il settore industriale in esame – per il controllo di un forno di riscaldo di billette all’interno di un’acciaieria, ottenendo un risparmio reale di gas naturale superiore al 7% annuo. Il sistema, coperto da brevetto, ha vinto il premio Cesef Energy Efficiency Award 2015 organizzato dal Centro Studi sull’Efficienza Energetica dell’Università Bocconi di Milano come progetto di efficienza energetica più innovativo in Italia.

L' articolo di i.Process "Improving Performances of a Cement Rotary Kiln: A Model Predictive Control Solution", pubblicato sul Journal of Automation and Control Engineering, Vol. 4, No. 4, pp. 262-267, August, 2016, é stato premiato come una delle migliori pubblicazioni in occasione del 8th International Conference on Environmental and Computer Science (ICECS) 2015.

Menzione dell'APC sviluppato da i.Process nel documento dell'International Energy Agency, Insights Series 2013, "Energy Provider-Delivered Energy Efficiency, a global stock-taking based on case studies".

Pubblicazioni

n.42

Astolfi, Barboni, Barchiesi, Cocchioni, Orlietti, Rocchi, Pepe, Zanoli – Università Politecnica delle Marche – “A training framework for i.Process/Steel-RHF” 3th Estad, 26-29 June 2017, Vienna

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This paper describes the operators’ training methodology developed with i.Process/Steel-RHF Advanced Process Control system. i.Process/Steel-RHF is an Advanced Process Control system that aims at controlling and optimizing steel reheating furnaces. i.Process/Steel-RHF APC system has been installed on five steel industries, located in different European countries, obtaining energy efficiency and processes control improvements. A key phase for i.Process/Steel-RHF mission is represented by the operators’ training phase; it constitutes a very crucial phase for the system safety point of view and for the acceptance of the new (automatic) Advanced Process Control system by human operators. All these aspects strongly affect the service factor of the i.Process/Steel-RHF Advanced Process Control system. i.Process/Steel-RHF Advanced Process Control system includes an ad hoc training framework that moreover assure a significant speed up of the operators’ training phase. The control system, together with the operators’ training methodology, has been created by the staff of i.Process S.r.l., through a cooperation with Università Politecnica delle Marche D.I.I. L.I.S.A

n.41

Astolfi, Barboni, Barchiesi, Cocchioni, Orlietti, Rocchi, Pepe, Zanoli – Università Politecnica delle Marche – “i.Process/Steel-RHF: an adaptive algorithm aimed at online optimization of re-heating furnances” 3th Estad, 26-29 June 2017, Vienna

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This paper describes an Advanced Process Control system, named as i.Process/SteelRHF, aimed at the control and the optimization of steel reheating furnaces. The control system has been developed by the staff of i.Process S.r.l., through a cooperation with UNIVPM (Università Politecnica delle Marche) D.I.I. (Dipartimento di Ingegneria dell’Informazione) L.I.S.A. (Laboratory for Interconnected Systems Supervision and Automation). i.Process S.r.l. is an Italian company dedicated to rethinking innovation for energy saving. i.Process S.r.l. is focused on carrying out automation engineering activities and developing Advanced Process Control systems to optimize the control of industrial processes and reduce energy consumption and production costs. i.Process/Steel-RHF APC system has been installed on five steel industries, located in different European countries. Energy efficiency certificates, together with an improvement on processes control, have been obtained in all real applications. The developed reheating furnaces control method has been awarded with an Italian patent. Furthermore, the pioneer i.Process/Steel-RHF project has been awarded with a significant energy efficiency award.

n.40

Astolfi, Barboni, Cocchioni, Pepe, Zanoli, - Università Politecnica delle Marche – “Optimization of a pusher type reheating furnace: an adaptive Model Predictive Control approach”, Adconip 2017, the 6th International Symposium on Advanced Control of Industrial Processes, May 28-31, 2017, Taipei Taiwan

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In this paper, an Advanced Process Control system based on a two-layer linear Model Predictive Control strategy is proposed. The control system aims at optimizing a pusher type billets reheating furnace, located in an Italian steel plant. A first principles nonlinear model has been developed, in order to obtain estimations of billets temperature inside the furnace. A Linear Parameter-Varying model for billets temperature has been accordingly derived. To obtain a global modellization of the furnace unit, an additional black-box approach has been adopted for the internal process dynamics. The overall resulting model has been exploited for the design of the Model Predictive Control scheme. Performances on an industrial process have shown the major profitability of the proposed control solution with respect to the previous one, based on a suitable handling of local PID controllers. In particular, significant energy saving has been obtained, together with an improved specifications fulfillment.

n.39

Astolfi, Barboni, Barchiesi, Cocchioni, Orlietti, Rocchi, Pepe – i.Process – Zanoli – Università Politecnica delle Marche – “Optimised clinker production” International Cement Review, April 2017

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The need for increased energy efficiency in clinker production has led to a wide range of innovations in cement production and has seen an increasing sophistication of automation and process control solutions.

n.38

Zanoli, Pepe, – Università Politecnica delle Marche – “Two-Layer Linear MPC Approach Aimed at Walking Beam Billets Reheating Furnace Optimization”- Journal of Control Science and Engineering Volume 2017, 31 January 2017

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In this paper, the problem of the control and optimization of a walking beam billets reheating furnace located in an Italian steel plant is analyzed. An ad hoc Advanced Process Control framework has been developed, based on a two-layer linear Model Predictive Control architecture. This control block optimizes the steady and transient states of the considered process. Two main problems have been addressed. First, in order to manage all process conditions, a tailored module defines the process variables set to be included in the control problem. In particular, a unified approach for the selection on the control inputs to be used for control objectives related to the process outputs is guaranteed. The impact of the proposed method on the controller formulation is also detailed. Second, an innovative mathematical approach for stoichiometric ratios constraints handling has been proposed, together with their introduction in the controller optimization problems. The designed control system has been installed on a real plant, replacing operators’ mental model in the conduction of local PID controllers. After two years from the first startup, a strong energy efficiency improvement has been observed.

n.37

Pepe, Zanoli, Cocchioni – Università Politecnica delle Marche – “Energy saving and environmental impact decreasing in a walking beam reheating furnace” – WIT Transactions on Ecology and Environment, Volume 205 2016, Energy Quest 2016

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This book contains a selection of papers presented at the 2nd International Conference on Energy Production and Management in the 21st Century, “The Quest for Sustainable Energy”, organized by the Wessex Institute of Technology, UK, the Marche Polytechnic University, Italy, and the Ural Federal University, Russia. The papers deal with many of the current issues related to energy production and management. The quest for Energy can be seen as a unique long journey spanning over the history of humanity. As a matter of fact, humans have spent much of their lives struggling to achieve, gather, and manage energy. In modern societies, the consumption of energy is one of the main indicators of the level of development. The rapid growth of the world population and the demand for higher living standards has led to the massive consumption of non-renewable energy sources with serious environmental consequences. While in the past many of these problems were localised, today one must talk of “global” pollution and the zone adversely affected is simply the whole world, mainly due to the extensive use of fossil fuels. There is an urgent need to increase the use of renewable sources of energy. Yet, the transition from an economy based on conventional energy to one relying on renewable sources presents massive challenges. It requires new scientific and technological progress related not only to energy production but also to new ways of distribution, storage and usage, aiming to mitigate the negative environmental impacts associated with conventional energy. In this context, the complexity of modern energy production and management requires a multidisciplinary approach that can take into consideration not only advances in technology but also involves the environmental, economic, social and political aspects. In such framework, the contributions in this volume present recent developments and experiences from different parts of the world.

n.36

Pepe, Zanoli – Università Politecnica delle Marche – “The importance of cooperation and consistency in two-layer Model Predictive Control” 17th International Canternational Carpathian Control Conference ICCC’2016 May 29 - June 1, 2016, High Tatras, Tatranská Lomnica, Slovak Republic

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In this paper a two-layer linear Model Predictive Control strategy is proposed. The problem of establishing a consistency between the mathematical formulations of the two layers is analyzed. At the lower layer, a basic Model Predictive Control module computes the future control moves taking into account predictions on the manipulated and controlled variables. In the quadratic optimization problem that characterizes this module, constraints over the prediction and control horizons and steady state optimal targets need to be properly set. At this purpose, at the upper layer of the control system architecture, a Targets Optimizing and Constraints Softening module has been introduced. This module is characterized by the minimization of a linear cost function, subject to linear inequality constraints. Its objective is the search of optimal steady state targets, assuring, for example, an economic and/or chemical optimal operative condition, while meeting steady state process constraints. A solution that guarantees the consistency between the formulations of the two layers is proposed, defining a cooperation between them. Practical results deriving from the introduction of the proposed architecture in a control system that drives a pusher type reheating furnace located in an Italian steel plant have proven the soundness and the reliability of the proposed approach.

n.35

Pepe, Zanoli – Università Politecnica delle Marche – “A two-layer Model Predictive Control system with adaptation to variables status values” 17th International Canternational Carpathian Control Conference ICCC’2016 May 29 - June 1, 2016, High Tatras, Tatranská Lomnica, Slovak Republic

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This paper proposes a formulation of a linear Model Predictive Control system based on two layers. The formulation takes into account a status value associated with each variable. At the lower layer, a basic Model Predictive Control module minimizes a constrained quadratic cost function for the computation of the future control moves. At the upper layer, an additional module, based on a Linear Programming problem, searches for optimal steady state targets. To obtain resilience of the control system in particular conditions, at each control instant, the introduction of a parameter, the status value, associated to each process variable, is proposed. At this purpose, a suitable additional module has been introduced in the considered architecture that contributes to the definition of the status of each manipulated or controlled variable. Ad hoc modifications in the mathematical formulations of the optimization problems of the two layers are proposed to include the information provided by the status values. This strategy has been introduced in a control system that optimizes a pusher type reheating furnace of an Italian steel plant. Simulation results show the validity of the proposed approach. Field results prove the reliability of the designed control scheme, in terms of economic optimization, environmental impact reduction and productivity/product quality maximization.

n.34

Pepe, Zanoli – Università Politecnica delle Marche –“Input moves selection in Model Predictive Control: decoupling approach”- The 42nd Annual Conference of IEEE Industrial Electronics Society October 24-27, 2016. Firenze Italy

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In this work an Advanced Process Control module based on a two-layer linear Model Predictive Control architecture is presented. The two layers, exploiting model-based predictions and suitably cooperating, optimize both the dynamic and steady state behaviors of the considered process. Furthermore, each layer is equipped with a decoupling strategy developed in order to provide a selection of the manipulated inputs to be adopted for the control tasks of each single controlled output. The proposed architecture has been introduced for the regulation of the heating phase of billets processing in an Italian steel industry. Simulations have shown the validity of the proposed approach. Results from the real plant have shown significant improvements from previous control solutions, based on advanced PID architectures manually driven by plant operators. In particular the operating points of the process have been moved to more profitable ones, leading, together with the multivariable approach, to energy saving and pollution impact decreasing, complying with safety and quality requirements.

n.33

Zanoli, Pepe, Rocchi – Università Politecnica delle Marche – “Improving Performances of a Cement Rotary Kiln: A Model Predictive Control Solution”- Journal of Automation and Control Engineering Vol. 4, No. 4, August 2016

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In this work an advanced control system design aimed to the improvement of economic benefits and control performances of a cement rotary kiln located in an Italian cement plant is discussed. A Model Predictive Controller, together with other functional blocks designed to manage normal and critical situations, constitutes the core of the proposed strategy. Accurate identification procedures, aimed at obtaining accurate dynamical process models, have been performed. A suited cooperation of system modules and an ad hoc design of each of them allowed the meeting of control specifications, the increase of system reliability and the reduction of the standard deviation of critic process variables. In this way, the system can more safely operate closer to its operative bounds. The implementation of the proposed control system on a real plant has proven its soundness, leading to improvements in terms of energy efficiency, product quality and environmental impact, compared to the previous control system.

n.32

Zanoli, Pepe, – Università Politecnica delle Marche – “A constraints softening decoupling strategy oriented to time delays handling with Model Predictive Control” - 2016 American Control Conference (ACC) Boston Marriott Copley Place
July 6-8, 2016. Boston, MA, USA

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In this work a two-layer Model Predictive Control strategy is presented. The upper layer searches for optimal steady state targets, while the lower layer manages the transient state of the considered process, calculating the future control moves. In order to efficiently handle time delays on the input-output channels, a suitable lower layer formulation has been developed. In particular, a decoupling strategy acting on the constraints relaxation guarantees a protection against critical situations tied to the presence of time delays. In the proposed Advanced Process Control system architecture, the Model Predictive Control module interacts with other key modules that assure major resilience in the plant management. Simulation and practical results deriving from the implementation of the designed controller on a clinker production phase have proven the consistency and the reliability of the proposed approach. With respect to the previous control system, based on local PID controllers, a remarkable variance reduction of the controlled variables has been observed, leading to the possibility to run the plant much closer to the operating boundary limits thus achieving, besides all, energy saving.

n.31

Barboni, Sartini – “Adaptive Process Control and DMC3 migration in api raffineria di Ancona SpA” - 2016 User Group Meeting - Advanced Control and Optimization World User Group (ACOWUG) – Terneuzen (Netherlands) – Maggio 2016

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n.30

Zanoli, Pepe, Rocchi – Università Politecnica delle Marche – “Control and Optimization of a Cement Rotary Kiln: a Model Predictive Control approach” - Indian Institute of Technology Hyderabad January 4-6, 2016. 2016 Indian Control Conference (ICC)

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In this paper, a Model Predictive Control strategy is used to stabilize a temperature profile along a cement rotary kiln minimizing fuel specific consumption. The adopted system architecture is composed of two different optimization layers that interact in order to improve control performances and to meet possibly variable economic goals. The developed cooperation logic between the two layers that takes into account priority ranking of controlled variables constraints has been proven to improve system performances. The developed MPC system has been implemented on a cement plant and its performances are compared to the performances of the previous system based on standard PID controllers. The results are very satisfactory in terms of economic benefits and of minimization of environmental noxious emissions.

n.29

Zanoli, Pepe, Barboni – Università Politecnica delle Marche – “Application of Advanced Process Control Techniques to a pusher type reheating furnace” – Journal of Physics: conference series 659 – 12th European Workshop on Advanced Control and Diagnosis (ACD 2015) – 2015

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In this paper an Advanced Process Control system aimed at controlling and optimizing a pusher type reheating furnace located in an Italian steel plant is proposed. The designed controller replaced the previous control system, based on PID controllers manually conducted by process operators. A two-layer Model Predictive Control architecture has been adopted that, exploiting a chemical, physical and economic modelling of the process, overcomes the limitations of plant operators’ mental model and knowledge. In addition, an ad hoc decoupling strategy has been implemented, allowing the selection of the manipulated variables to be used for the control of each single process variable. Finally, in order to improve the system flexibility and resilience, the controller has been equipped with a supervision module. A profitable trade-off between conflicting specifications, e.g. safety, quality and production constraints, energy saving and pollution impact, has been guaranteed. Simulation tests and real plant results demonstrated the soundness and the reliability of the proposed system.

n.28

Astolfi, Barboni, Cocchioni - “Sistema di controllo avanzato per un forno walking-beam” – Automazione e Strumentazione – Novembre/Dicembre 2015

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Questo articolo descrive un innovativo Sistema di Controllo Avanzato (APC - Advaced Process Control) applicato ad un forno di preriscaldo (di tipo Walking Beam) di billette di acciaio. All’interno dell’APC è implementato un Modello Termodinamico Adattativo agli Elementi Finiti (FEM - Finite Element Method) in grado di stimare il riscaldamento di ogni billetta del forno; le stime delle temperature di ogni billetta sono utilizzate all’interno di un algoritmo di Controllo Predittivo (MPC - Model based Predictive Control) in grado di regolare la combustione di ogni zona del forno al fine di riscaldare correttamente ogni billetta (fino alla temperatura di sfornamento desiderata), minimizzando il consumo specifico calcolato come il rapporto tra il gas naturale impiegato per la combustione [Sm3] e le tonnellate relative alle billette sfornate [ton]. La riduzione del consumo specifico di gas naturale, ottenuta dopo un anno di funzionamento (con un service factor del sistema APC intorno al 98%) è stata prossima al 5%. L’applicazione della medesima tecnologia ad un forno di preriscaldo di tipo Pusher Type ha prodotto una riduzione del consumo specifico di gas naturale pari al 15% (dopo 3 mesi di funzionamento). Gli aspetti innovativi introdotti hanno consentito la premiazione del sistema APC all’interno degli Energy Efficiency Awards 2015 organizzati dal Centro Studi sull’Economia e il Management dell’Efficienza Energetica (CESEF) di Milano e la sottomissione di una domanda di brevetto europeo.

n.27

Zanoli, Pepe, Rocchi – Università Politecnica delle Marche – “Cement Rotary Kiln: constraints handling and optimization via Model Predictive Control techniques” 5th Australian Control Conference (AUCC) November 5-6, 2015. Gold Coast, Australia

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In this work an Advanced Process Control approach aimed to the control and the optimization of a cement rotary kiln is proposed. Model Predictive Control techniques have been adopted for the controller and a system architecture based on two optimization layers is proposed. A suitable design of each layer and their interaction policy allowed to improve control performances and to meet possibly variable economic goals. Simulation results show the effectiveness of the adopted control architecture and optimization problem formulation. Results of practical implementation of the proposed controller on a cement rotary kiln unit confirm the improvement of the performances in terms of energy efficiency, product quality and environmental impact when compared to the previous control system.

n.26

Zanoli, Pepe, Orlietti, Barchiesi – Università Politecnica delle Marche “A Model Predictive Control Strategy for Energy Saving and User Comfort Features in Building Automation” 19th International Conference on System Theory, Control and Computing (ICSTCC), October 14-16, 2015 Cheile Gradistei, Romania

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Home & Building Automation provides automatic control of indoor environments and its primary goals are the fulfillment of user requirements while realizing significant energy savings. In the present paper, energy saving in home and office buildings is achieved through an efficient management of heat and lighting devices and with the exploitation of renewable resources, like sun (thermal and light energy) that takes into account user comfort. The presence of coupling effects between the thermal and illuminance systems (mainly in shutter actuation) that often calls for contrasting requests in terms of actuators control actions, adds difficulties to the controller design if approached with standard control methods. An improvement of a previous control system is performed by the authors by adopting Model Predictive Control techniques. The proposed system integrates energy-consuming sources for heat and light power supply, with green energy-supplying sources. To handle the nonlinearities a switching approach has been adopted in developing the Model Predictive Controller, which is based on a bank of models dependent on thermal and light solar radiation. The proposed solution allows obtaining satisfactory performances in terms of regulation and energy efficiency; in addition, exploiting green energy-supplying sources, a sensible reduction of energy consumption is achieved.

n.25

Zanoli, Pepe, Rocchi, Astolfi – Università Politecnica delle Marche “Application of Advanced Process Control techniques for a Cement Rotary Kiln”- 19th International Conference on System Theory, Control and Computing (ICSTCC), October 14-16, 2015 Cheile Gradistei, Romania

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In this work, the control optimization of a rotary kiln located in a cement plant is described. The need to enhance the efficiency level, to increase the profitability as well as the commitment to meet precise production standards has motivated the adoption of Model Predictive Control techniques. The adopted system architecture is composed of two different optimization layers. A suitable interaction policy has been developed so as to improve control performances and to meet possibly variable economic goals. The advantages of the proposed architecture are shown by significant simulations. The developed predictive control system has been implemented on real plant and its overall performances are compared to the performances of the previous standard PID control system. Very satisfactory system performances have been achieved been able to attain both economic benefits and minimization of environmental noxious emissions.

n.24

Lamenza, Valle, Barboni – “Un caso di Advanced Process Control” – FLUID Trasmissioni di Potenza – Maggio 2015

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L’efficienza energetica è un argomento che ricorre spesso in queste pagine: è stato trattato in relazione alla manutenzione e alle utilities, sono stati esposti i criteri con cui individuare le sacche di inefficienza e ne sono state illustrate le tecniche disponibili per rimuoverle. Qui si presenta un intervento in cui è proprio l’efficienza energetica del processo ad essere incrementata.

n.23

Astolfi, Barboni, Barchiesi - “Sistema di controllo avanzato applicati a centrali termiche” – Automazione e Strumentazione – Gennaio/Febbraio 2015

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Nel corso degli ultimi anni, per la maggior parte delle industrie di processo, lo scenario di mercato è cambiato in modo molto evidente. Si è passati d un contesto a moderata competizione, tipicamente locale, ad un contesto con competizione esasperata e globale. Contemporaneamente, i costi delle materie prime e delle utilities sono cresciuti significativamente, contribuendo a comprimere ulteriormente i margini industriali. In questo contesto altamente competitivo, l'automazione di processo si è dovuta evolvere al fine di garantire incrementi di efficienza ed efficacia della produzione. Questo tipo di evoluzione ha visto l'introduzione sempre più marcata di sistemi di controllo avanzato (APC) ed ottimizzazione il cui obiettivo fontamentale è la massimizzazione dei profitti aziendali attraverso una maggiore efficienza nell'uso degli assett aziendali.

n.22

Zanoli, Orlietti Barboni – Università Politecnica delle Marche – “Steam Reforming Plant Optimization with Model Predictive Control” – ETFA13 – 18th IEEE International Conference on Emerging Technologies & Factory Automation – September 10-13 2013, Cagliari (Italy)

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In this work the optimization process of a steam reforming unit located in a petrochemical plant is described. The need to enhance the efficiency level, to increase the profitability as well as the commitment to meet precise production standards has motivated the adoption of Model Predictive Control (MPC) techniques. The proposed MPC system has been implemented on a real plant and its performances are compared with the performances obtained with previous PID controllers.

n.21

Zanoli, Astolfi – Università Politecnica delle Marche – “Application of first-principles based techniques for compounds prediction in a gasification plant” European Control Conference (ECC) July 17-19, 2013, Zürich, Switzerland

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In this work a first-principles based model for the prediction of the Syngas composition produced in a gasification plant is described. The model design is based on chemicalphysical principles of the gasification process and is based on nonlinear static equations. For the formulation of the equations, Gibbs energy minimization of the compound used in the gasification process is taken into account together with computations of material balance. The model has been validated on real data from a gasification plant. From the analysis of the results, it can be stated that good estimation performances have been achieved. This chemical-physical model overcomes the limitation introduced by the use of a linear model presented by the authors in a previous work. In fact, the new model provides a good estimation even when the range of the stoichiometric ratio is extended out of validity values of a linear model. Given the results, the model can be used in a Model-Based Predictive Control (MPC) strategy for process optimization and/or for the development of a Fault Diagnosis system.

n.20

Zanoli, Barchiesi, Astolfi, Barboni – Università Politecnica delle Marche – “Advanced control solutions to increase efficiency of a furnace combustion process” – ECC13 - European Control Conference – Zurich, July 17-19, 2013

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In the present work the problem of the furnaces combustion optimization in petrochemical environment is presented. In particular, the paper is focused on the combustion efficiency that directly affects the operating costs of the plant. A preliminary study of the combustion process has been performed. A model of the system has been obtained by a black-box approach and limitations of the existing control architecture have been analyzed. A new control architecture, based on advanced PID control architecture, coupled “crosslimiting” control logics and Fuzzy logic has been developed and implemented in a Distributed Control System (DCS). The major benefits introduced by the new control system can be found in its reliability and in its robustness to compensate the measurable disturbances that affect the furnace. Moreover, the proposed control scheme has been proven to be effective in the reduction of the O2 content in the exhaust of furnace gas as well as in the reduction of the fuel consumption. As a consequence of the O2 reduction a reduction of the exhaust gas temperature has been achieved thus further increasing the furnace efficiency. The total efficiency increase has been estimated of about 2.2% with a significant energy saving of about 500 k€/year. Finally, the reduction of nitrogen oxide and carbon monoxide concentrations in the exhaust gases achieved by the new control strategy, allows minimizing the pollution emissions satisfying the actual national environmental requirements.

n.19

Zanoli, Orlietti, Astolfi, Barboni – Università Politecnica delle Marche – “Clustering Data Procedure for the Prediction of the Recovered Volume of the Light Gasoil of a Visbreaking Column” – MED2012 - 20th Mediterranean Conference on Control and Automation – July 2012, Barcelona

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In this work the model identification of a visbreaking column for the estimation of the recovered volume at 360◦C of Gasoil is considered and a clustering procedure for the selection of the identification dataset is presented. A high valuable product for the visbreaking process is the light gasoil; Its purity can be measured by the recovered volume at 360◦C and, for control purposes, an on-line estimation of this property is very important. In this paper a new procedure for predicting the light gasoil recovered volume is presented; the approach is based on the use of a clustering Fuzzy CMeans algorithm for the selection of the input data used in the identification process. Results are presented which prove the goodness of the proposed procedure and the reliability of the estimated model in the prediction of the gasoil recovered volume.

n.18

Zanoli, Barchiesi, Astolfi, Barboni – Università Politecnica delle Marche – “Head Pressure minimization of a Visbreaking column through an advanced PID controllers architecture” – IFAC Conference on Advances in PID Control, PID'12, Brescia, 28-30 March 2012

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In this paper an advanced PID control architecture developed to minimize the head pressure of a visbreaking column is presented. The implemented control strategy has been designed through an advanced interconnection of PID controllers typically used in industrial processes: cascade control, feedforward control and override control. In order to correctly identify the interactions of the key variables, a RGA analysis has been performed. The proposed control system has been previously tested on simulation, thus been able to evaluate its performances in terms of robustness, stability and rejection of the main disturbances of the process. The results on the plant of the column optimization confirmed its effectiveness in the minimization of the head pressure. In this way, the new controller guaranties an increase of the separation between the heavy and light components and an increase of the extraction of the valuable products like gasoline and Light GasOil. The application of the proposed control architecture has allowed reaching an important economic recovery.

n.17

Zanoli, Astolfi, Barboni – Università Politecnica delle Marche – “Application of a new dataset selection procedure for the prediction of the Syngas composition of a gasification plant” – ADCHEM 2012, the 8th IFAC International Symposium on Advanced Control of Chemical Processes, The International Federation of Automatic Control - Furama Riverfront, Singapore, July 10-13, 2012

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In this paper the model identification of a gasification process for the estimation of the Syngas composition is considered and a new procedure for the selection of the identification dataset is proposed. Estimations are needed to integrate the gascromathographic measurements of the Syngas composition which are often not available because of periodic calibrations. This work is part of a broader project for the development of a supervisory controller for process optimization and for fault detection and isolation scope. Improvements in the identification process from standard procedures have been obtained by means of a suitable selection of the input data set. The proposed input data selection procedure is based on the application of the Fuzzy C-means (FCM) algorithm for the generation of the main clusters. Results on a gasification process of a refinery plant show the effectiveness of the proposed FCM method in filtering a large dataset and the reliability of the model in the prediction of the Syngas composition.

n.16

Zanoli, Barchiesi, Barboni – Università Politecnica delle Marche – “Thermal and Lighting Control System With Energy Saving and Users Comfort Features” – MED2012, The 20th Mediterranean Conference on Control and Automation. July, 2012 – Barcelona

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Building automation systems (BAS) provide automatic control of indoor environments conditions. Their primary goal is to realize significant energy savings, to reduce costs and to increase users comfort. In this paper improvements of the control system of a Building and Home Automation system previously presented by the author are presented. The system integrates energy-consuming sources for heat and light power supply, such as heat pumps and artificial lights with green energy-supplying sources like natural radiation and natural illuminance. In particular, in the present work, control solutions as a new thermal control policy, an anti-glare logic, and a logic for accounting of the solar radiation are introduced. This controller works satisfactory reducing the need for energy-consuming sources and it reaches good control performances and energy efficiency by making the best of the advantages of intelligence building.

n.15

Astolfi, Barboni – “Ottimizzazione della combustione di un forno di raffineria” – Automazione e Strumentazione – September, 2012

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In questo articolo si presenta un innovativo schema di controllo impiegato per la regolazione della combustione di un forno di raffineria orientata all’ottimizzazione dell’efficienza termica. La strategia di controllo permette di mantenere costante il rapporto stechiometrico tra l’aria e il fuel gas e di minimizzare la concentrazione di O2 nei fumi. L’efficienza del forno è stata incrementata del 2% comportando un ritorno economico di circa 200.000 €/ anno. La stessa soluzione può essere estesa a tutti i forni di una raffineria: considerando una lavorazione media di 3,3 Mton/anno di greggio, si è stimato un rientro economico minimo di 1 milione €/anno per ogni campagna di ottimizzazione dei controlli combustione.

n.14

Zanoli, Astolfi, – Università Politecnica delle Marche – “Faults Diagnosis for a centrifugal machine using the Mahalanobis distance” 8th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS) August 29-31, 2012. Mexico City, Mexico

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The paper proposes a fault diagnosis procedure based on a model-free approach and the use of pattern recognition techniques. In particular this paper aims to improve the isolation performance of a Fuzzy Faults Classifier (FFC) previously proposed by the author by the use of the Mahalanobis distance as metric for identifying the most probable fault. The proposed approach is applied to an industrial multishaft centrifugal compressor located in an Air Separation Unit (ASU). In particular faults due to the wear and tear of the thrust bearing and to fouling of the compressor stage are considered. The case study confirms the goodness of the overall procedure in the detections of both single as well as multiple faults and shows the improvements of the proposed approach in terms of the FDI system promptness.

n.13

Zanoli, Astolfi, – Università Politecnica delle Marche – “Application of a Mahalanobis-based Pattern Recognition technique for Fault Diagnosis on a chemical process” 20th Mediterranean Conference on Control & Automation (MED) Barcelona, Spain, July 3-6, 2012

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The paper proposes a Fault Detection and Isolation (FDI) procedure based on a model-free approach and the use of pattern recognition techniques. In particular this paper aims to improve the isolation performance of a Fuzzy Faults Classifier (FFC) previously proposed by the author by modifications of the fuzzification module and by the use of the Mahalanobis distance as metric for identifying the most probable fault. In the paper faults due to the wear and tear of the thrust bearing and to fouling of the compressor stage of an industrial multishaft centrifugal compressor are considered. The presented results show the goodness of the overall procedure in the detections of single as well as multiple faults and its promptness in terms of faults isolation.

n.12

Zanoli, Astolfi, Marczyk – Università Politecnica delle Marche; Ontonix s.r.l – “Complexity-based methodology for Fault Diagnosis: application on a centrifugal machine” – 3th IFAC Conference on Analysis and Control of Chaotic Systems (IFAC CHAOS 12), June 20 -22, 2012 Cancún, México

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In this paper a novel approach for the detection and the isolation of typical faults in oil refinery plants is presented. The proposed approach is based on a complexity-based methodology that allows monitoring a complex system by a holistic vision and provides a metric for the complexity measurements of the system. Applications for FDI of a centrifugal compressor are considered. Since this metric resulted sensitive to change in the operative conditions of the plant under study, for Fault Diagnosis purposes, a proper filtering procedure has been developed. System data relative to different operative conditions have been clustered and the information has been used to discriminate between variations of the complexity measure due to system failure from the one strictly related to changes in the operative conditions. The validity of the proposed method has been tested on real data concerning a fault occurred in a centrifugal compressor located in the Air Separation Unit (ASU) of a refinement plant.

n.11

Zanoli, Astolfi, Barboni – Università Politecnica delle Marche – “Prediction of the syngas composition of a gasification process” – 5th International Symposium on Communications, Control and Signal Processing - May 2 – 4, 2012, Rome, Italy

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The scope of this work is the development of a mathematical model of a gasification process to be used for the prediction of the Syngas composition. The predictions are intended to support the gascromathographic measurements of the Syngas composition which are often not available due to periodic calibrations. This work represents the first step of broader project which scope is the development of a supervisory controller to perform process optimization and fault detection and isolation. The original contribution of the paper is the design of a new procedure for the selection of the dataset used to design and to identify the model; the approach is based on the use of the Fuzzy C-means (FCM) algorithm for the generation of the main clusters used as input data in the identification process. The presented results show the goodness of the proposed FCM method in filtering a large dataset and the reliability of the identified model in the prediction of the Syngas composition.

n.10

Zanoli, Astolfi – Università Politecnica delle Marche – “A Solution to the Principal Components Selection in PCA and its Application in Chemical Processes” – 9th ACD 2011 European Workshop on Advanced Control and Diagnosis Regular Paper Danubius Hotel Gellert, Budapest, Hungary 17-18 November, 2011

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In the present paper a new method for the choice of the Principal Components (PCs) as part on the development of a fault diagnosis system is proposed and a comparison with other criteria present in literature has been made. The present research, motivated by some difficulties encountered on the application of standard criteria, aims to find a rigorous way to determine the dimension of the PC subspace when approaching fault diagnosis problems with PCA techniques in real contexts such as refinery plants. Major benefits of the proposed selection criteria based on statistical test ANOVA can be found in its reliability, the possibility to apply to both covariance and correlation data matrix together with its objectiveness and uniqueness. The effectiveness and efficiency of the proposed approach is illustrated by considering its application in the development of a fault diagnosis system for a multishaft centrifugal compressor.

n.9

Zanoli, Barchiesi, Barboni – Università Politecnica delle Marche – “Coupled Controller for Energy Saving in Building Automation based on a Thermodynamic and Illumination model” – 19th Mediterranean Conference on Control and Automation, Aquis Corfu Holiday Palace, Corfu, Greece - June 20-23, 2011

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Reduction of energy consumption in buildings is presently a key aspect with respect to the reduction of C02 emissions which is increasing every day its importance. Energy saving can be achieved through an efficient management of heat and lighting devices and with the exploitation of renewable sources, like sun. In the present paper a Building and Home Automation system that integrates energy-consuming sources for heat and light power supply, such as heat pumps and artificial lights with green energy-supplying sources like fresh air flow and natural illuminance has been proposed. A model of the thermodynamic and lighting behavior of a building has been preliminarily developed which is essential for the simulations and tests of the proposed Building Automation control policies. In the proposed control system, techniques of natural heating and daylight penetration are used to minimize electric lighting as well as electric heating that are integrated in an interconnected control loop. In order to balance the energy savings and comfort of users suitable control logic have been implemented.

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Zanoli, Astolfi, Barboni – Università Politecnica delle Marche – “First-priniciples based model for prediction of syngas composition in a gasification plant” – 2011

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In this work a first-principles based model for the prediction of the Syngas composition produced in a gasification plant is described. The model is based on the chemical-physical principles of the gasification process and is developed by a system of nonlinear static equations. Some of these equations are based on the Gibbs energy minimization of the compound used in the gasification process others equations taking into account the material balance of the process. The results is a quadratic system where the number of the unknowns equals the number of the equations and its resolution leads toward an unique solution. The model has been validated with real data gathered from the plant and a good prediction can be ascertained. This model can be used in a Model-Based Predictive Control (MPC) strategy for process optimization and/or for the development of a Fault Diagnosis system.

n.7

Zanoli, Astolfi, Barboni – Università Politecnica delle Marche – “Fault Detection and Isolation system for a Multishaft Centrifugal Compressor” – Offshore Mediterranean Conference March 2011 Ravenna, Italy

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In the present work the design and the implementation of a Fault Detection and Isolation (FDI) System for a multishaft centrifugal compressor used in an IGCC (Integrated Gasification & Combined Cycle) of refinement plant is described. The system has been developed for the Detection and the Isolation of the faults which may damage the compressor: single and multiple faults has been considered that concern errors in the sensor readings and/or in the actuators used in the machine as well as process faults like dirtiness of the compressor stages and break of the thrust bearing. The Detection and the Isolation of faults has been performed by a new approach that combines Principal Component Analysis, Cluster Analysis and Pattern Recognition. The Principal Component Analysis (PCA) technique, a multivariable data driven analysis method, has been adopted for monitoring the chemical process performances. An innovative procedure for the determination of the number of principal components based on the statistical test ANOVA (ANalysis Of VAriance) is introduced. The Clustering Analysis (CA) has been developed to overcome the growth of the complexity in the analysis of process faults that typically involve many variables; typical faults have been described by a specific fault prototypes that may be compared with the actual operative condition in order to recognize the potential fault. Finally the Pattern Recognition (PR) has been performed by an automatic online procedure that compares the stored fault prototypes with the actual operative condition and, in case of fault, automatically indicates the current fault. The proposed approach have been tested and validated on the real plant data and its goodness and effectiveness could be proven.

n.6

Zanoli, Astolfi, Barboni – Università Politecnica delle Marche – “FDI of Process Faults based on PCA and Cluster Analysis” – October 2010 Conference on Control and Fault Tolerant Systems – Nizza, France

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A new approach to fault detection and isolation that combines Principal Component Analysis (PCA), Clustering and Pattern Recognition is presented. Single, multiple faults which may cause errors in the sensor readings and/or in the actuators as well as process faults are considered. Determination of the number of principal components is based on the statistical test ANOVA following the approach proposed by the authors in previous works. To overcome to the growth of complexity in the analysis of process faults that typically involve many variables, an automatic procedure for the isolation of the principal known faults has been developed. The proposed methodology which is based on Clustering and Pattern Recognition Analysis represents the new contribution of the present paper. The method is tested on experimental data from an IGCC (Integrated Gasification & Combined Cycle) section of an oil refinery plant to monitor a compression’s process. Results show the goodness and effectiveness of the proposed approach on process faults detection and isolation.

n.5

S. M. Zanoli, G. Astolfi, L. Barboni, “Principal Component Analysis based on ANOVA Test for Multishaft Centrifugal Compressor Fault Detection and Diagnosis” – 4th IFAC Symposium on System, Structure and Control (SSSC’10), September 15-17 2010, Ancona (Italy)

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In the present paper a Fault Diagnosis system for a multishaft Centrifugal compressor included in an Integrated Gasification and Combined Cycle section of a refinement plant is illustrated. The Principal Component Analysis (PCA) technique, a multivariable data driven analysis method, has been adopted for monitoring the chemical process performances. The contribution of the paper is a new ANOVA-based procedure for the determination of the number of principal components. Both single and multiple faults which may cause errors in the sensor readings and/or in the process actuators have been considered. Trial results proved the goodness and the effectiveness of the proposed diagnoser on detection and isolation of faults assuring a satisfactory promptness as well.

n.4

Zanoli, Astolfi, Barboni – Università Politecnica delle Marche – “Applications of Fault Diagnosis Techniques for a Multishaft Centrifugal Compressor” – MED'10 June 23-25, 2010 – Marrakech, Morocco

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In the present paper the design and implementation of a Fault Diagnosis system for a compression’s process integrated in an IGCC (Integrated Gasification & Combined Cycle) section of a refinement plant is described. Both single and multiple faults have been considered which may cause errors in the sensor readings and/or in the actuators used in the process. A multivariable data-driven approach, that is a principal component analysis (PCA) technique has been adopted for monitoring the chemical process performances. A new procedure for the determination of number of principal components based on the statistical test ANOVA is introduced which constitutes the original contribution of the paper. The proposed approach on detection and isolation of faults have been tested and validated on the plant and its goodness and effectiveness could be proven.

n.3

Zanoli, Barboni – Università Politecnica delle Marche – “A DCS Supervisory Control of a Centrifugal Compressor for Oxygen Consumption Optimization" – MED'09 June 24-26, 2009 – Thessalonik, Greece

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In this paper, a supervisory control system for oxygen consumption optimization on a Syngas Manufacturing Process Plant is proposed. A grey-box multivariable parametric identification of the oxygen compressor system is first performed. Consequently, by means of dynamic simulations the structure of an optimal control system has been determined, also reflecting the implementation constraints linked with the use of a DCS. Finally, operating results of the system implemented on the real process are shown which confirmed the expected results obtained by simulations.

n.2

Zanoli, Barboni, Leo – Università Politecnica delle Marche – “An application of hybrid automata for the MIMO model identification of a gasification plant” – 2007 IEEE International Conference on Systems, Man, and Cybernetics, Oct. 8, 2007. Montréal, Canada

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In this paper hybrid automata have been used in order to identify the more suitable model of a gasification process, at the occurrence of variations of plant’s operative conditions and specifications. From the identification process performed on historical series at disposal and from the model validation phase it was concluded that one single model was not sufficient to assure good predictions, especially in the case of large prediction horizons. This called, from a methodological point of view for the application of a hybrid model, in particular of hybrid automata in order to guarantee best performances in the choice of the best model. Performances of the proposed hybrid model are discussed and compared with the ones obtained with a fuzzy supervisor based on the same linear models.

n.1

Zanoli, Barboni, Leo – Università Politecnica delle Marche – “Hybrid Model of a Gasification Plant”, 16th IFAC World Congress, (Praha – 07/2005)

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In this paper, a hybrid model of a gasification unit is presented, using linear representations and a discrete events (DES) supervisor based on automata. This supervisor chooses the best linear description at the occurrence of variations of plant’s operative conditions, to assure the best prediction performance. Performances of the proposed hybrid model are discussed.