Italiano

SEI - Smart supervisor for Energy efficiency optimization of Industrial processes

REGIONE PUGLIA PO FESR 2007 - 2013
PROGRAMMA PLURIENNALE DI ATTUAZIONE PERIODO 2007-2013
Asse I – Promozione, valorizzazione e diffusione della ricerca e dell’innovazione per la competitività
Azione 1.1.3: Aiuti alle nuove imprese innovatrici che investono in ricerca e sviluppo.
Aiuti alle piccole imprese innovative di nuova costituzione

Goals

Continuous energy efficiency

 

Operating costs reduction

Optimization of investments for production plants

Online evalutation​ of KPIs

solution

SmartSupervisor

Idea75's decision support system

RESULTS

6

plants managed by SmartSupervisor

+3.100

controlled equipment

+3.800 k€

investments made thanks to decision support

-1.100 k€

of energy consumption

 
Real time iiot dashboard
 
GOALS
The goal of the project was to create an innovative decision support system for continuous energy efficiency and predictive maintenance of energy-intensive industrial plants to:

 

reduce the operating costs

  • with lower energy consumption,

  • with less occurrences of unscheduled downtime,

 

optimize the investments for production plants

  • online evaluation of the efficiency KPIs of the equipment and the lines of the plant

  • sharing and certification of data for project financing interventions by ESCo, credit institutions and / or public co-financing tenders.

Plansichters of the grinding department controlled by SmartSupervisor

 
SOLUTION

Each SmartProcessor is associated with one equipment

Idea75 developed SmartSupervisor, a distributed computing cluster designed following an architectural approach able to guarantee great flexibility and modularity to be perfectly compliant to the enabling technologies of Industry 4.0.
The calculation cluster is based on the SmartProcessor device, a compact SBC (Single Board Computer) encapsulated in a "USB form factor" enclosure.
The SmartProcessors firmware is equipped with uSS (micro SmartScada) and uIOES (micro IOES), two algorithms for the energy efficiency of industrial plants appropriately optimized for the management of a single machine (equipment).

SmartSupervisor: the SmartProcessors cluster

SmartSupervisor acquires information from the field and other high-level systems. The system is entirely remotely manageable

 
RESULTS
SmartSupervisor:
  • is installed in 6 production plants of the Casillo Group, located between Corato, Altamura and Lucca;

  • monitors and manages 3196 equipment;

  • led to the achievement of 7% savings through the reduction of energy consumption, maintenance costs and optimization of production;

  • led to the EnPI reduction of 4.48% compared to the 2013 baseline value

  • proved to be an effective investment with an IRR (internal rate of return) of 41% and an ROI of 121% (assessed over four years).

Types of monitored equipment

 

Installations of SmartSupervisor

SCIENTIFIC publications, papers and awards

GRASPA 2015 (biennial conference of the Italian Research Group for Environmental Statistics): “Energy-efficiency optimization of the biomass pelleting process by using statistical indicators”

June 14, 2015

Biomass pelleting process strongly depends on a number of variables hard to be simultaneously controlled. This paper suggests a method to ensure pellets moisture optimization and process energy saving. An experimental testbed was arranged in order to validate the performance of the proposed strategy. It is based on a closed-loop control system that regulates material moisture and flow rate, but its robustness is affected by the control-loop delay (the actuator delay is about 10 minutes) and by the random arrangement of the pellets inside the cooler that strongly affects product moisture (the measurement errors are not negligible). To overcome those problems, a robust statistical approach was adopted to reach the best tradeoff between estimation accuracy and computational effort. It was derived by the well known Random Close Packing model and statistical estimator. Experimental results prove the effectiveness of the proposed approach that provides moisture errors less than 7.2% with a continuous limitation of energy consumption.

Giuseppe L. Cascella, Davide Cascella, Francesco Cupertino, "Energy Metering Optimization in Flour Mill Plants for ISO 50001 Implementation," 2016 IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems, Bari, Italy, 13-14 June 2016

June 12, 2016

This paper proposes an innovative strategy to optimize the energy metering in large and energy-consuming plants such as industrial flour mills. The proposed solution deals with ISO 50001 implementation which represents a critical challenge for many companies because the benefits due to improvements in energy management could be potentially canceled by the costs of an Energy Management System (EnMS). In particular, the Key Performance Indexes (KPIs) monitoring is a crucial activity for several reasons: it is one of the early activities, it affects the measurement quality of KPIs and it deeply impacts the EnMS requirements and, consequently, the investment valuations. The proposed strategy supports the energy managers in the design of the energy monitoring system suggesting the points of the electrical network to be equipped with sensors and monitored. Moreover, the paper describes the results carried out in a real-world application: the energy sensor network of a 1.2MW flour mill plant sited in Italy has been designed and implemented with the proposed innovative solution.

Lovato Electric: “Per una trasformazione del grano più efficiente” [AUTOMAZIONE INDUSTRIALE n. 240 – marzo 2016]

March 06, 2016

"Fondamentale per raggiungere gli obiettivi di efficienza energetica è una rete di monitoraggio dei flussi energetici basata su strumenti di misura capaci di rilevare in tempo reale e registrare tutte le grandezze utili per qualificare il funzionamento dell'impianto. [...] Determinante per la realizzazione del progetto si è rivelata la collaborazione con l'azienda di progettazione ed engineering Idea75. Il decision support del sistema proprietario Smart Supervisor di Idea75 ha consentito la stima dei KPI più significativi".

Il Sole 24 Ore 24/11/2016: “Assemblee. Il presidente De Bartolomeo: Pronti al livello 4.0. Le tecnologie avanzate trainano l’industria di Bari”

November 23, 2016

"Industria 4.0 deve coinvolgere tutto il tessuto produttivo nazionale e consentire la digitalizzazione non solo delle grandi aziende, ma anche delle Pmi. Un obiettivo bilaterale che a Bari ha già molti buoni esempi. Come il gruppo Casillo (azienda leader nel settore del grano e delle farine) che opera in collaborazione strategica con la start-up Idea75, nata nel 2014, che propone soluzioni per l'ottimizzazione dei processi, l'efficientamento di impianti ed il monitoraggio/controllo di impianti industriali di aziende energivore."

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stakeholders & Credits

Giuseppe Leonardo Cascella, PhD

PROJECT COORDINATOR

Idea75

MSc with honors and PhD in Electrical Eng. @Politecnico di Bari (IT)

EU Marie Curie Fellow @Nottingham University (UK)

40+ international papers

30+ industrial and R&D projects

Francesco Cupertino, Full Professor

SCIENTIFIC DIRECTOR

Politecnico di Bari

MSc with honors and PhD in Electrical Eng. @Politecnico di Bari (IT)

Full professor of Converters, Electrical Machines and Drives @Politecnico di Bari

Scientific Director @Energy Factory Bari

100+ international papers

Regione Puglia

Funding body

Funding body and promoter of the 2007-2013 FESR Operational Program for the enhancement and dissemination of research and innovation for regional competitiveness.

Puglia Sviluppo

Facilitating measure responsible

Puglia Sviluppo manages incentive tools for companies as an intermediate body of the Puglia Region or a direct manager of financial instruments.

gruppo-casillo.png

Casillo Group

The Casillo Group, with a milling and handling capacity of over 2 million tonnes / year of wheat, is one of the world's largest "market makers" in the durum wheat sector.

 
KEYWORDS

SmartScada

SmartProcessors

uSS

uIOES

Decision Support System

Energy Efficiency

Industry 4.0

Milling plants

Parallel computing

Distributed computing

Cluster