1,200 spm Laboratory park, bot-based (blueprint inside)

Clever and beautiful constructions, bigger than two chunks
- Defense: killing biters as an art
- Castles, Throne Rooms, Decorations (comfortable living in the Factorio World)
- Main Bus Concepts
- Modular Systems, Factory Streets, show how all works together
- Megabases
Please provide us with blueprints or saves, if that makes sense of course.
Forum rules
Clever and beautiful constructions, bigger than two chunks
Post Reply
vinnief
Inserter
Inserter
Posts: 31
Joined: Sat Feb 10, 2018 3:39 am
Contact:

1,200 spm Laboratory park, bot-based (blueprint inside)

Post by vinnief »

I like designing the stations around my factories. This factory has space for 14 trains (1.1 type) to deliver science and use bots to feed the beaconed labs. Each train has to deliver a single color science pack. All of the unloading stations have the same name, and there is a circuit which intelligently unloads the train so you don't have to worry about producing science in the correct ratio - if you are overproducing a pack, the unloading station will transfer the science packs at the correct rate to ensure that the labs are fed evenly and there is no uneven build-up of science. The science is stored in buffer chests with enough room for 8 minutes at full production of 1,200 science per minute.

Trains delivering science also get refueled. There is an additional station for delivering fuel - just send a 1.1 train there with fuel in the wagon, and the robots will take care of unloading the fuel and distributing it to the other science stations.

If you deploy this blueprint you will need to add around 500 bots.

Complete list of features:

* 1,200 science per minute with beaconed labs and bots

* Buffers up to 8 minutes of science (at full output)

* Flexible unloading station, trains deliver any single color, all stations have the same name. You can also send multiple trains with the same color science pack and the unloader will do the right thing.

* Separate station for delivering train fuel

* Low profile attachment to the rail system (small intersection)

* Large setback (distance from the intersection to the factory). This helps keep the logistics network isolated (a requirement)

* Long lead-in and lead-out from the intersection: trains enter the complex at full speed and don't slow down until they are inside. When leaving, trains have room to accelerate so they exit the intersection at full speed. This reduces the impact of traffic at the intersection

* Solar panels and accumulators to fill in some gaps for aesthetics and to provide a small discount on power consumption.

Blueprint:

Code: Select all

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
labs.png
labs.png (5.63 MiB) Viewed 4534 times
Last edited by vinnief on Tue Feb 13, 2018 5:38 pm, edited 1 time in total.

Aeternus
Filter Inserter
Filter Inserter
Posts: 835
Joined: Wed Mar 29, 2017 2:10 am
Contact:

Re: 1,200 spm Laboratory park, bot-based (blueprint inside)

Post by Aeternus »

Neat design, but there's room for improvement! Assuming you've got one train per research type, why not dedicate 2 stations per research type, and based on whichever station has least stuff buffered, send the train there? This way you can omit the buffer chests closest to the research buildings entirely, as their function is handled by the stations themselves.

vinnief
Inserter
Inserter
Posts: 31
Joined: Sat Feb 10, 2018 3:39 am
Contact:

Re: 1,200 spm Laboratory park, bot-based (blueprint inside)

Post by vinnief »

That's a great idea, but one of the design goals for this project was to avoid having dedicated stations

Post Reply

Return to “Medium/Big/Gigantic Sized Structures”