Last June, the Green and Connected Ports project (GREEN C PORTS), led by the Fundación Valenciaport and funded by the European Commission’s Connecting Europe Facility (CEF) programme, was launched. GREEN C PORTS aims to provide a suitable array of digitalization tools and technologies to support port environmental sustainability and performance of port operations in the TEN-T Core Network.
This project will address six business cases consisting of prototypes and pilot tests that will be implemented in different European ports and that will serve as a basis to test innovative technologies such as IoT, big data or predictive analysis using artificial intelligence models.
The first business case, which will be held in the port of Valencia, seeks to integrate different platforms, sensor networks and sources of information to predict the date and time of entry and departure of trucks using predictive analytics and business intelligence tools. In this way, and by achieving high accuracy in the predictions made, it will be possible to determine how many trucks/hour will leave and enter the port at a certain future date and time.
The second business case aims to predict the closure of the Port of Venice due to tide, wind, fog, and consequently to optimize date and time of entry and departure of ships using predictive analytics and big data tools This prediction system will allow the ships a safer and more efficient organisation of their trip, avoiding the only solution currently put in place: to close the port with very short notice entailing consequent changes of navigation routes or long waiting times for vessels that were about to call at it and as a result, economic and port performance loss..
On the other hand, business cases 3 and 4 aim to improve air quality and noise in both the Greek port of Piraeus and the port of Valencia. In this regard, a series of sensors, meteorological databases, optical-imaging cameras and other equipment will be deployed, in order to predict air and noise quality levels in a near future date and time. These predictions will be of great interest to the port authority, city council and other government institutions so that they can take certain decisions that mitigate these impacts.
Likewise, the fifth business case will evaluate, in the German ports of Bremerhaven and Wilhemshaven, how ship to shore (STS) crane productivity is affected by wave agitation, currents and wind. Wilhemshaven). By being able to model together big data originated by different port IT systems (i.e. PCS, PMS, TOS…) a set of warnings will be sent to affected parties such as terminal operators and sea carriers when expected reductions in port productivity are expected. Once this information is reported, shipping companies will be able to adjust the “berth window” in which they call at the ports, reducing as far as possible the length of the ship’s stay in port and the number of polluting emissions that these ships generate.
Finally, the sixth and last business case will evaluate the impact in terms of emissions of a series of goods from the time they are loaded in the warehouse of origin to the time they are unloaded in the warehouse of destination. A series of sensors and emission cameras will be installed so that carbon emissions can be determined for each of the products that are transported. Thanks to this pilot case study, companies in the retailing sector will be able to inform their customers about the door to door carbon footprint of the products to be purchased in the company’s supermarkets.