The goal of BayesMassBal is to allow users to easily conduct Bayesian data reconciliation for a linearly constrained chemical or particulate process at steady state.

Samples taken from a chemical process are always observed with noise. Using data reconciliation, or mass balance methods, it is possible to use the principle of conservation of mass to filter the noise. This technique is common in chemical engineering and mineral processing engineering applications.

Typically, a mass balance produces point estimates of true mass flow
rates. However, using Bayesian methods one can obtain a more granular
view of process uncertainty. The `BayesMassBal`

package provides
functions allowing the user to easily specify conservation of mass
constraints, organize collected data, conduct a Bayesian mass balance
using various error structures, and select the best model for their data
using Bayes Factors.

The Bayesian mass balance uses Markov chain Monte Carlo methods to obtain random samples from the distributions of constrained mass flow rates. These samples can be used to generate plots, or for other applications where sampling from such a distribution is useful.

You can install the released version of BayesMassBal from CRAN with:

```
install.packages("BayesMassBal")
```

`BayesMassBal`

After loading the package

```
library(BayesMassBal)
```

Functions are available to aid in Bayesian data reconciliation.

- The
`importObservations()`

function can be used to import mass flow rate data from a`*.csv`

file into`R`

and organize it for use with the`BayesMassBal`

package. - Toy data sets can be simulated using the
`twonodeSim()`

function for educational purposes, or for comparing the performance of Bayesian data reconciliation methods to other methods. - Using the
`constrainProcess()`

function, one can specify linear constraints in`R`

or import them from a`*.csv`

file. - The
**B**ayesian**M**ass**B**alance function,`BMB()`

, then can be used to generate samples from target distributions and approximate the log marginal likelihood for a specified model. - A summary table can be viewed in the console and saved using
`summary.BayesMassBal()`

. - The output from
`BMB()`

is a`"BayesMassBal"`

object, which can be fed to`plot.BayesMassBal()`

to easily plot the results. - A
`"BayesMassBal"`

object can also be used with the`BayesMassBal`

function`mainEff()`

to inspect how the main effect of a random variable and uncertainty in process performance are related.

An overview of a suggested workflow, including importing data into `R`

,
specifying model constraints, using the `BMB`

function, and making a
main effects plot, is available as a vignette:
`vignette("Two_Node_Process")`

.

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