The current increasing production of small volume, high added value products has called
attention... more The current increasing production of small volume, high added value products has called attention to batch production technologies. Although batch distillation typically consumes more energy than continuous distillation, it provides more flexibility and involves lower capital investment. Thus, since energy costs are not too significant in the separation of small volume of high profit products, batch distillation is often attractive. Available batch distillation models have appeared in the literature, ranging from very simple approaches to more detailed. Commercial software packages such as BATCHFRAC™ (Boston et al. 1981), BASIS (Simulation Sciences Inc., 1989) and ProSimBatch, (1992) are available only to model batch distillation operation. Luyben W. (1992) implemented an inferential control using a rigorous quasi-dynamic model of a batch distillation column. Recent progress in nonlinear model-based control techniques have made the practical applicability of nonlinear controller a true. Many of these techniques use a nonlinear dynamic process model directly in the control law development phase (which is performed off-line). These features are very attractive for practical implementation. Henson and Seborg (1997) list a number of simulation and experimental studies in which this feedback controllers were used. In this context, a nonlinear model–based strategy implemented with commercial software is analyzed here. Therefore BATCHFRAC™, HYSYS® and CHEMCAD® performances were tested to evaluate whether their implementation in an inferential control structure and /or soft sensor could be attractive. Hence, using the worldwide commercial available tools, the dynamic behavior of a batch distillation column was modeled.
The current increasing production of small volume, high added value products has called
attention... more The current increasing production of small volume, high added value products has called attention to batch production technologies. Although batch distillation typically consumes more energy than continuous distillation, it provides more flexibility and involves lower capital investment. Thus, since energy costs are not too significant in the separation of small volume of high profit products, batch distillation is often attractive. Available batch distillation models have appeared in the literature, ranging from very simple approaches to more detailed. Commercial software packages such as BATCHFRAC™ (Boston et al. 1981), BASIS (Simulation Sciences Inc., 1989) and ProSimBatch, (1992) are available only to model batch distillation operation. Luyben W. (1992) implemented an inferential control using a rigorous quasi-dynamic model of a batch distillation column. Recent progress in nonlinear model-based control techniques have made the practical applicability of nonlinear controller a true. Many of these techniques use a nonlinear dynamic process model directly in the control law development phase (which is performed off-line). These features are very attractive for practical implementation. Henson and Seborg (1997) list a number of simulation and experimental studies in which this feedback controllers were used. In this context, a nonlinear model–based strategy implemented with commercial software is analyzed here. Therefore BATCHFRAC™, HYSYS® and CHEMCAD® performances were tested to evaluate whether their implementation in an inferential control structure and /or soft sensor could be attractive. Hence, using the worldwide commercial available tools, the dynamic behavior of a batch distillation column was modeled.
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attention to batch production technologies. Although batch distillation typically consumes more
energy than continuous distillation, it provides more flexibility and involves lower capital
investment. Thus, since energy costs are not too significant in the separation of small volume of
high profit products, batch distillation is often attractive.
Available batch distillation models have appeared in the literature, ranging from very simple
approaches to more detailed. Commercial software packages such as BATCHFRAC™ (Boston
et al. 1981), BASIS (Simulation Sciences Inc., 1989) and ProSimBatch, (1992) are available
only to model batch distillation operation.
Luyben W. (1992) implemented an inferential control using a rigorous quasi-dynamic model of
a batch distillation column. Recent progress in nonlinear model-based control techniques have
made the practical applicability of nonlinear controller a true. Many of these techniques use a
nonlinear dynamic process model directly in the control law development phase (which is
performed off-line). These features are very attractive for practical implementation. Henson
and Seborg (1997) list a number of simulation and experimental studies in which this
feedback controllers were used. In this context, a nonlinear model–based strategy implemented
with commercial software is analyzed here. Therefore BATCHFRAC™, HYSYS® and
CHEMCAD® performances were tested to evaluate whether their implementation in an
inferential control structure and /or soft sensor could be attractive.
Hence, using the worldwide commercial available tools, the dynamic behavior of a batch
distillation column was modeled.
attention to batch production technologies. Although batch distillation typically consumes more
energy than continuous distillation, it provides more flexibility and involves lower capital
investment. Thus, since energy costs are not too significant in the separation of small volume of
high profit products, batch distillation is often attractive.
Available batch distillation models have appeared in the literature, ranging from very simple
approaches to more detailed. Commercial software packages such as BATCHFRAC™ (Boston
et al. 1981), BASIS (Simulation Sciences Inc., 1989) and ProSimBatch, (1992) are available
only to model batch distillation operation.
Luyben W. (1992) implemented an inferential control using a rigorous quasi-dynamic model of
a batch distillation column. Recent progress in nonlinear model-based control techniques have
made the practical applicability of nonlinear controller a true. Many of these techniques use a
nonlinear dynamic process model directly in the control law development phase (which is
performed off-line). These features are very attractive for practical implementation. Henson
and Seborg (1997) list a number of simulation and experimental studies in which this
feedback controllers were used. In this context, a nonlinear model–based strategy implemented
with commercial software is analyzed here. Therefore BATCHFRAC™, HYSYS® and
CHEMCAD® performances were tested to evaluate whether their implementation in an
inferential control structure and /or soft sensor could be attractive.
Hence, using the worldwide commercial available tools, the dynamic behavior of a batch
distillation column was modeled.