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- gracicot2
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Reliable, constant and balanced 2 compressed blue belt per wagon side
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Postby gracicot2 »
I'm not sure if train station can get better in term of throughput.
This train station design is a combination of techniques and designs I found online. It can achieve a constant 4 blue belt per wagon, or 2 blue belt per wagon side.
The problems I found online with designs for a 2 blue belt per wagon sides were:
- Couldn't compress the blue belt and leave gaps
- Unbalanced
- Couldn't get the next train fast enough to output 100% constant compressed blue belt
- Not compact
This design is an attempt to fix those problems. When I tried it, it seemed to be working but the timings are quite tight for the 16 belt unloader. It requires the next train to be waiting right before the current one to have a constant output. The balancing has a small bias and allow small unbalance-ness to be able to have a constant output.
There's blueprint for the 8 lanes version, working with a 2 wagon train.
This design might be optimizable to be more compact, but it is quite compact already and pretty elegant. Loading and unloading stating have the same footprint, and input/output at the same place.
Caveats:
- Need the next train to be right behind the parked one
- Needs circuits for the balancing
- Technically not the fastest to load and onload because inserters are doing the balancing (parked time not minimized)
Blueprint:
Code: Select all
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*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*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*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*ckHi2PD6MiqVYnk1GidtijKBYmuWdY3Dlq/EIm799ft68vNy+fH7Y7kYX9B/EB28yp9WgAITczwCUG5HzDLlDkdh8daEjx4QNhquMBrLtPNlEMe/OkcsUuXiOXOGZXl3whmQrtcp0bscbRa6eWZ2ntOXX2LPR6rwzyDCflaGntCadOzg+UOT8OaYN2rLygYerTAay/jxZSmtSPMd8ocidXR2lLens8W4GGZ7/kAVKa34NhxvJMFA2Jvtzq6O05deIueHqDDZm5W8MV2nQmnz83C6QpxEMOrTK64EgWQJxGEiRxOWQXf*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*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*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- 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datarza
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Re: Reliable, constant and balanced 2 compressed blue belt per wagon side
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Postby datarza »
Good job @gracicot2, please consider one of the following two updates related to loading cargo wagons:
- test unloading.png (3.75 MiB) Viewed 1642 times
1. additional belt in the left side for stretch the loading line
2. 2x6 balancer with additional belt in the right side
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datarza
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Re: Reliable, constant and balanced 2 compressed blue belt per wagon side
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Postby datarza »
Hi once again, I was played with loading trains blueprint, and would say (perhaps it is discussable) that instead of balancing the unloading chests to train, maybee here should be implemented the balancing of loading chests from belts plus line balancer, like it was done in unloading trains blueprint.
Someting like that (it is draft):
- Screenshot 2023-12-09 210315.png (1.16 MiB) Viewed 1482 times
Another possible option is using the line balancer (balancer for both lines on belt) behind.
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- gracicot2
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Re: Reliable, constant and balanced 2 compressed blue belt per wagon side
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Postby gracicot2 »
Interesting. Balancing from the belt to the chest would be better, but I couldn't figure out how to guarantee full throughput while doing this.
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- Tertius
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Re: Reliable, constant and balanced 2 compressed blue belt per wagon side
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Postby Tertius »
There is a strange looking but very balanced loader for 2 blue belts, evolved from some discussion in this forum. It's almost completely mechanically balanced. I added active balancing nonetheless to force it and to make sure it's still balanced if the input isn't completely compressed, but if you remove active balancing, it will take quite some time until you notice anything.
- Screenshot 2023-12-17 215017.png (230.24 KiB) Viewed 1330 times
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- Roaders
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Re: Reliable, constant and balanced 2 compressed blue belt per wagon side
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Postby Roaders »
Tertius wrote: ↑
Sun Dec 17, 2023 8:54 pm
There is a strange looking but very balanced loader for 2 blue belts, evolved from some discussion in this forum. It's almost completely mechanically balanced. I added active balancing nonetheless to force it and to make sure it's still balanced if the input isn't completely compressed, but if you remove active balancing, it will take quite some time until you notice anything.
This works great for my use case.
Can I ask why you have red and yellow belts in there? It seems to work just as well with blue belts everywhere.
Also why do you have one underground belt section that comes from nowhere?
Thanks
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- Tertius
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Re: Reliable, constant and balanced 2 compressed blue belt per wagon side
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Postby Tertius »
The single yellow top right yellow belt has no other function as to make the lower yellow belt piece sideload instead of merge with the vertical line.
The other non-blue pieces help balancing. If they were all blue, the chests will not fill as equally as if they are yellow/resp. red. This setup is the result of much manual testing.
The single underground belt is to force sideloading from the rightmost lane. It's crucial all items are put on the top lane of the horizontal belt only. It's also a separator between the wagons. If you replace this with a regular blue belt pice, you will notice chests going out of balanced fill between the rightmost wagons and the leftmost wagons, and the leftmost input belts will start to back up, so the overall throughput will suffer. Might not be visible with the active balancing I added, but if your remove the inserter wiring and rely on the mechanical balancing only, you will notice that.
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- ↳ PyMods
- ↳ Questions, reviews and ratings
- ↳ Modding Discussions
- ↳ Modding discussion
- ↳ Modding help
- ↳ Ideas and Requests For Mods
- ↳ Modding interface requests
- ↳ Implemented mod requests
- ↳ Implemented for 2.0
- ↳ Already exists
- ↳ Won't implement
- ↳ Documentation Improvement Requests
- ↳ Resolved Requests
- ↳ Development tools
- ↳ Tools
- ↳ Mod and installation managers
- ↳ Cheatsheets / Calculators / Viewers
- ↳ Development tools
- ↳ Special interest mods and libraries
- ↳ Modding Development
- ↳ Multiplayer / Dedicated Server
- ↳ Multiplayer Board
- ↳ Archive
- ↳ Maps and Scenarios
- ↳ Translations
- ↳ Texture Packs
- ↳ Fan Art
- ↳ Wiki Talk
- ↳ To be sorted
- ↳ Campaign / Scenario suggestions
