(204d) Optimal Operation Strategy for Batch Vacuum Evaporation Process By Iterative Dynamic Programming

A vacuum evaporation technique is widely
used in the food, bio, pharmacy, and semiconductor industries. The vacuum
evaporation processes concentrate liquid at lower temperature by lowering the
boiling point of the liquid in the vacuum condition. By operating at lower
temperature, the vacuum evaporation process prevents the thermal decomposition
of the liquid and alleviates the corrosion problems of the materials. However,
the vacuum evaporation process lowers the dew point of the generated steam as
well as the boiling point of the liquid. Because the lowered dew point of the
steam reduces the mean temperature difference in the condenser, the driving
force for the heat transfer is reduced in the low pressure condition. For this
reason, the condenser size is dramatically increased or the evaporation rate is
limited in the general vacuum evaporation process [1,2]. This is a very important issue in the some processes of handling
the corrosive liquid such as a strong acid. Because the corrosion-resistant
materials are very expensive, the condenser becomes the most important unit.
Generally, the vacuum evaporation process is performed in the batch mode. Because
the steam generation rate and the mean temperature difference are changed in
the batch operating mode, the condenser area requirement also varies depending
on the operating time. The condenser must be designed to meet the maximum
condenser area requirement among the whole operation time. The maximum condenser
area requirement is determined by the depressurizing curve and the heating
curve. In this research, we suggest an optimal operation strategy to minimize
the condenser area in a batch vacuum evaporation process for sulfuric acid
purification. This process concentrates dilute sulfuric acid drained from the
semiconductor wafer cleaning. We established the discrete-time dynamic model of
the sulfuric acid purification process using pilot plant data. The objective
function is to minimize the maximum condenser area requirement among the whole
operation time. The manipulated variables are operating pressure and heat
supply according to the operation time. Because this problem is highly
nonlinear and dynamic programing, we developed an iterative dynamic programming
algorithm to solve it. By using this characteristic of the minimizing the
maximum problem, this problem is converted to maximizing the mean temperature difference
and eliminating the peak point of condenser area requirement. Through the
optimal operation strategy, the system was depressurized as slowly as possible
within a range that the temperature never exceed the maximum temperature limit
(190°C). Simultaneously, the heater duty was
controlled to generate the optimal amount of the steam in response to the
operating pressure change. That means large amount of the steam is generated at
high pressure condition and small amount of the steam is generated at low
pressure condition. To validate the effect of it, the optimal operation was
compared with a conventional operation which is depressurizing quickly and supplying
constant heat duty as shown in figure 1. As a result, this optimal operation
strategy showed the great reduction of the condenser area and the operation
time. In case of the operation time is fixed to 100 min, the condenser area was
reduced from 11.6 m² to 2.9 m². In case of the condenser
area is fixed to 10 m², the operation time was reduced from 117 min
to 54 min.

Figure1. The simulation result of optimal
operation when operation time is 100 min.

1. Mao, W., H. Ma, and B. Wang, Performance of batch vacuum distillation
process with promoters on coke-plant wastewater treatment. Chemical Engineering Journal, 2010. 160(1): p. 232-238.

2. Hiroshi Ogata, and Norio Tanaka, Reduction of Waste in Semiconductor
Manufacturing Plant, Sulfuric Acid Recycling Technology. Oki
Technical Review 160, 1998. Vol.63: p. 41-44.