Introduction
Process design involves the entire procedure from raw materials to
products, combining multiple unit operations, such as reaction, heat
exchange, and separation. In the development of new production
processes, the design and performance of virtual plants are simulated.
In this study, the objective is to design an efficient ethylene oxide
(EO) production process.
EO is an important petrochemical product formed by the oxidation of
ethylene. It is converted to various compounds such as ethylene glycol
and ethanolamine, and is a starting material for fibers and detergents.
EO production is about 9,000 t/y1 in Japan. In the
production process, the oxidation of ethylene is accompanied by side
reactions:
\({2\ C}_{2}H_{4}+O_{2}\rightarrow{2\ C}_{2}H_{4}O\) (1)
\(C_{2}H_{4}+3O_{2}\rightarrow 2\text{CO}_{2}+{2H}_{2}O\) (2)
\({2\ C}_{2}H_{4}O+5\ O_{2}\rightarrow 4\ \text{CO}_{2}+{4\ H}_{2}O\)(3)
Eq. (1) is the main reaction to produce EO and it requires silver
catalysts (Ag2O). Eqs. (2,3) are side reactions that
produce carbon dioxide and water. The EO process has a risk of explosion
because Eq. (2) has a heat reaction of +1323 kJ/mol at 523 K. Therefore,
there is a restriction on reactor inlet compositions and
conversions2. As a result, unreacted gas is recycled,
and the reactor exhibits nonlinear and complex behaviors. Here, we
design the EO production process assuming that only these three
reactions proceed.
Much research has been conducted to improve the performance of EO
production. Zhou et al. used a reactor model to optimize design
variables such as inlet compositions of ethylene, oxygen, and carbon
dioxide, as well as reactor temperature and the amount of ethylene
dichloride 3. Lahiri et al. optimized design
variables, such as inlet compositions and gas flow, using a support
vector machine and a genetic algorithm for EO recycling. They verified
the results with an actual plant based on the optimized design
variables, and reported improved EO production rate and catalyst
selectivity4. Yang et al. performed modeling and
validations for steady-state, dynamic, and start-up operation conditions
with EO recycling. They speeded up the plant start-up with optimized
start-up profiles for inlet compositions, temperature, and
pressure5. Rahimpour et al. modeled catalyst
deactivation using artificial neural networks to determine the optimal
amount of ethylene dichloride6. Luo et al. integrated
reaction kinetics, a reactor model, and a catalyst deactivation model of
an industrial ethylene oxide reactor and compared the results with an
actual EO plant7. Nawaz et al. used Aspen Custom
Modeler to optimize several design variables, such as reactor
temperature and inlet compositions8. Peschel et al.
considered interactions between reaction concepts and the entire EO
process, and modeled the physical and chemical principles. They reported
reduced operating costs and reduced carbon dioxide
emissions9. Distillation columns for the removal of
carbon dioxide were optimized for several variables, such as temperature
and pressure10, 11.
Often, the values of design variables are optimized for each unit, such
as a reactor and a distillation column traditionally. Hence, effects
between units are not considered, and the optimized values of design
variables from these methods are not always accurate when considering
the entire process.
We propose a method for optimizing the entire process by considering
effects between the units. Therefore, all units must be optimized
simultaneously, with a very large number of design variables that need
to be optimized. For example, the design variables X for the EO process,
such as equipment and operating conditions, have 23 parameters. Assuming
there are ten candidates for each X, the total number of combinations
would be 1023, which requires a huge number of
simulations.
Hence, we propose a process design method based on Bayesian
optimization12 where X values that satisfy target
values of multiple objective variables Y, such as yield, are searched.
Then X will be optimized with a small number of simulations. We verify
the effectiveness of the proposed method by simulating an EO plant.