TLDR: Please help me build a smelting array that doesn’t get jammed with single pieces of stone / iron plates in the furnaces.
I’m playing a ribbon world and I’ve designed an industrial smelter, where a train full of ores pulls in, everything is smelted, then the same train leaves with all the finished products.
The problem is that furnaces get jammed with single stones or iron plates when I’m trying to make stone bricks or steel. I need all furnaces to empty completely so that they are ready for the next train which may hold different materials.
I can read the incoming material from the train (and there will never be mixed materials on a single train) and set the stack size accordingly, but the stack size behaviour isn’t exact - an inserter will grab only one item if only one item is available rather than waiting for a full stack. Maybe there’s a mod to change this behaviour?
Edit to add screenshot and blueprint
Blueprint
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
Use filter inserters?
The problem isn’t putting items into the furnace; but the leftovers at the end. For example, I send in a train with 2k stone so I should get 1k stone bricks back. Instead I get 998 stone bricks and four furnaces with a single stone left over in them. So I only ever want to put exactly two stone into a furnace so that the entire smelting array is completely empty by the end and ready for the next train (even if the next train has iron or copper to smelt)
Right, so it sounds like in total the amounts match, but they are unevenly distributed. I.e. at the end one furnace has 1 iron plate while another has 4.
If you give each furnace its own input chest, limit all chests so they are in total same amount as the train carriages, feed them with a loop so all ore ends up in a chest, and postpone inserting into furnaces until all chests are loaded, it will work.
To postpone insertion, a circuit latch that turns off when train arrives and on after a circuit timer would be my choice.
I’ve got a fully working solution now thanks to your help. It’s not optimal in terms of throughput but it is optimal in terms of the flavour I want for this world, which is exactly what I wanted
Cool! Can you share a picture? :D
This is version 3 of the smelter, with all the combinators combined into a little computer instead of spread everywhere. It works almost every time, but there’s a rare bug where sometimes the inserters start unloading the train before I’ve read the contents of the train… not sure how. But I’ve updated it so it can handle trains of any fill level as long as they carry a multiple of 100 items.
Nice layout! That is pretty compact, given the complexity.
Thank you! :)
That’s a good idea, I like that. It will be slower to get started but I couldn’t think of anything that would work in vanilla. Thanks!
Sounds like the best option.
Whitelist the ores you want, or black list plate and stone.
Do you insert directly from train to smelter, or do you have some intermediate buffer?
If you ensure the trains are always full, it should be possible to make it balance perfectly.
5 stack inserters remove material from each carriage (6th is reserved for coal) and load onto 5 red belts. These are balanced onto a single red belt and fed into 48 steel furnaces. Each furnace grabs from the belt as much as it wants, which means that I sometimes have furnaces with only one stone in each and they can’t smelt, plus my train won’t be refilled completely. I need to leave all furnaces completely empty ready for the next train which may hold copper or iron instead.