How to use ARIMA to predict time series?
ARIMA models try to capture autocorrelation in your time series in order to make predictions. It is composed of three main models: AR, MA and I. So, you build your model to predict future values based on a linear combination of past values, linear combination of past errors and a differencing term (I) that accounts basically for a trend. You should get more information here: Forecast: Principles and Practice
My question is: why do you believe the tank volume is predicted by its own past values? When you ask how to use ARIMA, it is straight forward, however, I am concerned that you are not think properly about your problem. Isn't there an exogenous variable you could use? Maybe try a different model? A physical one?
If you really want to use ARIMA, you need to check if your data is not a random walk, plot autocorrelation and partial autocorrelation functions to see analyze which coefficients are more appropriate for AR and MA terms. Check for stationarity. Or simply use autoarima although I don't recommend doing that for the first time.