Economics and optimization sets for the CC25 example simulation

After you have designed the base-case process, AVEVA Process Simulation can simultaneously optimize multiple process specifications to maximize or minimize an objective function. For this example, our objective is to minimize the overall operating cost for the process.

We use the model extension feature in AVEVA Process Simulation to add economic and greenhouse gas emission calculations to the Source and Pump models. To do this:

1. Press and hold Shift and then drag the Economics.Utility submodel onto each Source and Pump model instance in the simulation.

2. Select the utility type and view the utility cost associated with that model instance.

3. Press and hold Shift and then drag the Economics.SteamGHG submodel onto each steam Source instance in the simulation (LPS1, MPS1, and MPS2).

4. Select the calculation method and emission factor submodel. If you use the Boiler Efficiency method, also set the boiler efficiency (eta).

Alternatively, you can add the Economics.Utility submodel to multiple model instances at the same time. To do this:

5. Select all the desired model instances.

6. Right-click the selection, and then on the Submodels submenu, on the Add submenu, on the Economics submenu, select Utility.

In the Economics Library, the Summary model provides a central location to adjust global utility prices and view the overall economics of the process. By adding this model to the simulation, we can edit global utility prices and use the total calculated costs to perform an economic optimization. The following figure shows the utility prices that we use in this example. See the model help for the Summary model for an explanation of how we have estimated the default utility prices.

Figure 2: Economic summary model and global utility prices

The SummaryGHG model in the Economics Library provides a central location to view the total greenhouse gas (GHG) emissions of the process. We can also edit the Global Warming Potential (GWP) factors for methane (CH4) and nitrous oxide (N2O), which impact the final calculation of the CO2 equivalent (CO2e) emissions. For this simulation, we use the default GWP factors for CH4 and N2O.

Figure 3: GHG emission summary model and global GWP factors

In this example, we do not consider the raw material cost, product value, or cost associated with treatment of the wastewater stream. We have specified the simulation such that the feed and product flow rates remain effectively the same. Therefore, we assume that these costs are fixed and do not impact the overall optimization. A detailed analysis might consider the wastewater treatment cost with respect to the methanol concentration in the waste stream, but this is beyond the intended scope of this simulation.

We provide the Minimize operating cost optimization set to specify the objective function and constraints of the optimization. We used the following considerations when we selected the optimization variables and constraints:

• Based on the reaction kinetics, increasing the temperature of the reactor feed stream (E2.Tto) increases the overall methanol conversion, decreases the required methanol recycle rate, and decreases the duty of the E3 exchanger. We set the upper bound of the reactor outlet temperature to 400°C to meet the maximum temperature constraints of the material of construction.

• The reboil ratio of T1 impacts the condenser duty, reboiler duty, and reflux ratio of the column. To meet the product specifications, we specify the weight percentage of DME in the overhead product stream to ≥99.5wt%.

  

  Figure 4: Optimization set to minimize total utility cost