Reliable, constant and balanced 2 compressed blue belt per wagon side

Smart setups of railway stations, intelligent routing, solutions to complex train-routing problems.
Please provide - only if it makes sense of course - a blueprint of your creation.
Post Reply
gracicot2
Manual Inserter
Manual Inserter
Posts: 2
Joined: Mon Sep 18, 2023 6:10 pm
Contact:

Reliable, constant and balanced 2 compressed blue belt per wagon side

Post by 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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
Attachments
Screenshot_20230918_142341.png
Screenshot_20230918_142341.png (2.12 MiB) Viewed 1783 times
Screenshot_20230918_142300.png
Screenshot_20230918_142300.png (2.42 MiB) Viewed 1783 times

User avatar
datarza
Long Handed Inserter
Long Handed Inserter
Posts: 76
Joined: Thu Sep 15, 2022 1:55 am
Contact:

Re: Reliable, constant and balanced 2 compressed blue belt per wagon side

Post by datarza »

Good job @gracicot2, please consider one of the following two updates related to loading cargo wagons:
test unloading.png
test unloading.png (3.75 MiB) Viewed 1174 times
1. additional belt in the left side for stretch the loading line
2. 2x6 balancer with additional belt in the right side

User avatar
datarza
Long Handed Inserter
Long Handed Inserter
Posts: 76
Joined: Thu Sep 15, 2022 1:55 am
Contact:

Re: Reliable, constant and balanced 2 compressed blue belt per wagon side

Post by 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
Screenshot 2023-12-09 210315.png (1.16 MiB) Viewed 1014 times


Another possible option is using the line balancer (balancer for both lines on belt) behind.

gracicot2
Manual Inserter
Manual Inserter
Posts: 2
Joined: Mon Sep 18, 2023 6:10 pm
Contact:

Re: Reliable, constant and balanced 2 compressed blue belt per wagon side

Post by 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.

Tertius
Filter Inserter
Filter Inserter
Posts: 671
Joined: Fri Mar 19, 2021 5:58 pm
Contact:

Re: Reliable, constant and balanced 2 compressed blue belt per wagon side

Post by 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
Screenshot 2023-12-17 215017.png (230.24 KiB) Viewed 862 times

Post Reply

Return to “Railway Setups”