Our client is a market leader in oil field logistics and transload services.  They own and manage a nationwide network of transload terminals where they store and move millions of pounds of bulk sand, crude, and other materials to and from trucks, railcars, silos, and other containers.  Their services are a critical element in the energy supply chain in North America.

Problem

This transload company contracted with one of their clients to install 4 200 ft silos to store frac sand and more quickly process inbound and outbound loads from one of their facilities. The silo installation included a pit and bucket elevator system for unloading railcars, truck scales and gate valves for loading trucks, and an automation system to control all this equipment remotely from an onsite scale house.

The facility is owned operated by the transload provider, but the silos would be owned by the client and used exclusively for their product. The silos were constructed because this was one of the busiest terminals in the region, with an extremely high volume of trucks.

The facility is located in a small town on one of its major thoroughfares, and the long line of trucks waiting to be loaded had caused traffic blockage for the entire town, much to the annoyance of local residents.

The silos were designed to be managed using an piece of industrial automation software called a SCADA (short for “supervisory control and data acquisition”). This significant investment in mechanical equipment and software promised to increase throughput for the terminal. However, since the SCADA was provided by their client, the transload company had no control over the software, and limited access to its data.

Our client has a custom terminal management application, which we helped them build, and which manages inventory, bills of lading, transload billing, and all other aspects of on-site terminal operations. It also feeds data to report systems used by management and customers to make strategic decisions.

Any loads into and out of the silos needed to be tracked in the terminal management application in order to maintain accurate inventory, and to generate correct bills of lading. Additionally, the terminal management application needed to feed data into the SCADA system about the trucks being loaded through the silos, to ensure the correct product was loaded on each truck.

Solution

The SCADA system implemented at this site had limited integration capabilities. However, it was configured to write out a log of trucks unloaded and railcars loaded to a local MySQL database, in two separate tables. The system also had the ability to consume an XML feed of incoming trucks.

We had previously built the client a custom microservice service in Node.JS. It ran on an appliance installed at each of their facilities, and collected data from truck scales, interfacing with the terminal management application in the cloud through a REST API.

We modified this service, adding a microservice to provide the XML feed the SCADA required, pulling data from the terminal management application. We also added a service which would repeatedly poll the SCADA database for new and updated records in the relevant tables. Any new loads would be translated to the format required by the terminal management system’s REST API, and forwarded to that system, in as close to real time as possible.

Result

This project had a very tight deadline but we were able to execute quickly and deploy our solution within just a few days. Our client’s operators on-site were able to run the automation system and load trucks far more quickly, increasing throughput while decreasing traffic.

The automation system received the truck and product data it needed to correctly assign products. The operations team was able to generate bills of lading through the terminal management application, utilizing all the optimizations already present in that system.

Since all load and unload events occurring at the silos were automatically logged in the terminal management application, they were able to track inventory without manual double entry, saving time and avoiding errors.

The transload provider was able to exceed expectations for their client, and the terminal became the most productive site in the client’s entire logistics network.

Our client is a market leader in oil field logistics and transload services.  They own and manage a nationwide network of transload terminals where they store and move millions of pounds of bulk sand, crude, and other materials to and from trucks, railcars, silos, and other containers.  Their services are a critical element in the energy supply chain in North America.

Problem

At each of the client’s loading facilities, they have operate one or several truck scales, industrial scales large enough to weigh an entire 18-wheeler, truck and cab.

When a truck arrives on site, the facility operations staff weigh the truck in, then sends the truck to get loaded somewhere else on site. After loading, the truck is weighed again. So you have an initial weight (tare) and a final weight (gross). The difference gives you the weight actually loaded on the truck while it was on site (minus any fuel consumed during the time period).

The scales have a series of pressure-sensitive load cells, which are all connected to a metal box, called an “Indicator” which is located in the small office where the terminal operator sits, at a computer.

The client wanted to be able to have that scale value feed directly into their custom terminal management system, so the operator doesn’t have to manually type the number in, and risk incorrect entry. This application, by the way, is a web-based cloud application, not something running locally on the PC.

We found there was a wide variety of scale indicators from different vendors in use. Some were network-enabled with RJ-45 ethernet ports or even wireless ports, but a lot just had 9 pin serial ports. We found that if we connected a PC to the serial or ethernet port on the scale, via Hyperterminal, it was constantly streaming a steady flow of raw data. However, the format of that data varied, depending on the vendor.

Solution

We ended up building a Node.JS service, which would open a socket to the scale indicator, and read the raw data stream. We built separate profiles for the different formats used by different scale vendors, so the service could interpret them. We also added logic to ignore variations caused by wind, and cut down network chatter. Then we deployed this service on a little headless appliance PC at each terminal.

Initially, the service simply provided a REST API to allow us to request scale weights on demand, but this required port forwarding at each location, which was not always possible, and when it was, made setup more complicated. Later, we reworked it so the service would push the weight data up to the cloud whenever there was a change.

Either way, the terminal operator, sitting at the PC, entering data about the truck could simply click a button, and capture the weight from the scale inside the web application, cutting data entry errors down dramatically, and making the data entry process much smoother.

Result

The weights collected from these truck scales are used to automatically generate a bill of lading for each truck departing one of the client’s transload facilities. Feeding the scale data directly into the client’s custom web application, not allowed saved employees time improved truck processing efficiency, it also eliminated a source of errors, allowing the client to have confidence that the information printed on their bills of lading is correct.