Subject: The internet of things is here...

People have been talking about the Internet of Things (IoT) for years. But now we’re actually seeing this talk turn into practical applications. 

I think we’re still only scratching the surface, and the true potential of IoT has yet to be brought to bear in most industries. Take battery manufacturing for example.

At Co-efficient, one of our goals is to bring the advantages of the IoT to lead-acid battery production. I believe the major application here is multi-point control loops.

To understand what this means, you need to imagine a system where anything that can be measured IS measured – and its data stored in a distributed database. Furthermore, anything that can be controlled by a signal (like servo motors and other electro-mechanical systems) is controlled.

It’s a totally closed-loop system.

And in this system, adjustments don’t always have to be 1:1. For instance, with a simple control loop, you could easily take a downstream thickness reading and use that to adjust the paste hopper or paddle speed. But with a multi-point control loop, you could combine a downstream thickness reading with a moisture reading along with even more information, like the age of the paste in the hopper and its density, to make adjustments at ANY control point.

This would be incredibly beneficial, because it’s impossible for operators to do manually.

This entire process of measurements and adjustments is completely automated. After first logging the inputs and outputs, your system would learn the process signature then adjust any of the known control points to adapt in real-time.

You could use this control system to predict and improve plate quality and active material consistency.

A Thought Experiment

How would we go about applying IoT systems to lead pasting lines? What are the necessary control points? What data will we collect?

The first thing we would do is make a list of all the potential inputs and outputs. This might be a long list. But here are some priority items and some of the metadata you could track …

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  • Incoming grid – is it the correct thickness and shape? Are there any broken/deformed wires?

  • Paste – at what point in the process is it dropped from the mixer into the hopper? At what temperature, density, and water content? How long is it mixed? How much paste is in the hopper? How fast are the paddles in the hopper moving? What are the current manually adjusted thickness settings? What’s the line speed? What’s the flash-dry speed and temperature? Is the paste sticking to the grid properly, or are there voids?

  • Plates – are they the right shape (i.e. flat, not wedged)?

  • Sensors – what are the current readings of your thickness, moisture, weight, and temperature sensors?

The next step would be to identify the gaps in measurement and control. Where could sensors be added? Where could control components like motors and actuators be used to make adjustments?

Once you’ve done that, all the inputs and outputs listed above could be controlled using any combination desired to improve plate quality or remove plates using a reject station.

While this might seem too unrealistic or impractical, it’s not. It’s simply using data to steer processes automatically. Human decision-making is never completely removed from the process. Engineers and technicians will be needed to tweak the models and interpret the data. 

But instead of working on managing lines to keep a certain level of quality, their focus will shift to optimizing – finding more and more ways to improve quality beyond what was previously possible without the IoT.

Cheers,

Steve

P.S. For those really looking to sink their teeth into the advanced concepts, you can also talk to your equipment suppliers, or look at the documentation provided with your equipment as a refresher. 

Or just talk with your operators about what adjustments they make on a daily basis. Any of these adjustments that are currently manual, can be automated. 

With no sake of brevity, here is an expanded discussion of adding automation to the potential inputs and outputs, of the pasting lines.

 Incoming Grid: Is it the right thickness? Right shape? No broken wires? Is there flash or other deformities on the wires? Could this be detected by computer vision or a 3D sensing system? 

Paste: When is it dropped from the mixer into the hopper? What temperature is the paste? How long was it mixed? What’s the recipes, density, water content. How much is in the paste hopper? How fast are the paddles moving in the paste hopper? What’s the height of the paste hopper? What are the settings of the known thickness adjustment points for the paster (i.e. ones that are presently manually adjusted)? (i.e. hopper, belt spacing, for cloth and steel belt, drum, gap spacing adjustment for FOP, trowel rollers, etc). What’s the condition of the pasting machine (belts, drum(s), bearings, tooling)? What is the line speed, what is the flash-dry speed and temperature, air speed and moisture, exhaust rate? All the rollers! (Finish rollers, squeeze rollers, trowel rollers, paste feed rollers) and tooling like the pressure plate. What is the status of the rollers? Are they running true, what is their spacing, are the bearings in good condition?  

Is the paste being applied to where it is supposed to be applied? Or is it bleeding onto the lugs and frame. If so, put a sensor on it! If it’s detected, address it. 

Is the pasting machine applying a texture to the surface, or wave pattern? Is it being applied correctly? Measure the texture inline or offline, and use it to maintain the paster. (as seen on Drum and other styles of pasters). 

Has the paste stuck to the grid properly? Are there paste lumps on the plate? Detect these with a camera or 3D sensor, and remove or adjust. 3-D plate geometries, thickest spot, thinnest spot, curl, greatest deviation from centerline (lumps and local deformations). Plate porosity.

Inline or offline measurement of chemical composition - including additive levels, lead oxide chemistry and ratios, binder quantities, impurities, oxide particle size.

Track the age of paste in the hopper - how long has it been in there? How long has the mix been sitting? 

Who is the operator? Match the pasting line operators shift, time of day, record this with all of the process variables. 

What are the current readings of the sensors you have on the line? Thickness, Moisture content, weight, temperature (for all of these, either off-line or in-line sensors placed anywhere on the line, know these values as the plates travel before and after the flash dry). Are the plates of the right shape? (right size, right flatness, not wedged). Is the paste properly centered on the grid? i.e. over-pasted equally?

As an input, dynamic control of the water content of the mix, or ingredients of the mix, either by adjusting the batch mixer, or the newer continuous paste mixers. Continuous paste mixers can be smoothly adjusted for the amount of water and acid that are added, along with the other paste ingredients, based on what is detected on the plates. 

These sensors could automatically measure, automatically retract, and be integrated within any of the existing pasting line equipment. 

These inputs and outputs could each be controlled, to either improve the plate quality, in any of the aspects measured above, or any combination of the outputs could be used to either manually or automatically remove the plates from the lines by a reject station. 

Remember, it’s a many to many scenario - any of the combination of the outputs above can be used to adjust any combination of the inputs above.