A preliminary analysis of the data already has been discussed in 10.2139/ssrn.3812299. Raw data Raw measurement data is in the Excel files `day*_raw.xlsx`. Model Covariate and label scaler objects are serialized in joblib format in the following files: 20210812_y_transformer_co2_ammonia_reduced_feature_set 20210812_y_transformer__reduced_feature_set 20210812_x_scaler_reduced_feature_set Checkpoints of the models are in the `*.pth.tar` files. An example for loading the models is: from pyprocessta.model.tcn import TCNModelDropout model_cov = TCNModelDropout( input_chunk_length=8, output_chunk_length=1, num_layers=5, num_filters=16, kernel_size=6, dropout=0.3, weight_norm=True, batch_size=32, n_epochs=100, log_tensorboard=True, optimizer_kwargs={"lr": 2e-4}, ) model_cov.load_from_checkpoint('20210814_2amp_pip_model_reduced_feature_set_darts') which assumes that the checkpoints are placed as `model_best.pth.tar` in a folder called `20210812_2amp_pip_model_reduced_feature_set_darts`.

The authors would like to acknowledge the ACT ALIGN-CCUS Project (No 271501) and the ACT PrISMa Project (No 299659). The ALIGN-CCUS project has received funding from RVO (NL), FZJ/PtJ (DE), Gassnova (NOR), UEFISCDI (RO), BEIS (UK) and is cofunded by the European Commission under the Horizon 2020 programme ACT, Grant Agreement No 691712; www.alignccus.eu. The PrISMa Project is funded through the ACT programme (Accelerating CCS Technologies, Horizon2020 Project No 294766). Financial contributions made from: BEIS together with extra funding from NERC and EPSRC, UK; RCN, Norway; SFOE, Switzerland and US-DOE, USA, are gratefully acknowledged. Additional financial support from TOTAL and Equinor, is also gratefully acknowledged.