Plant-wide design and control of acetic acid dehydration system via heterogeneous azeotropic distillation and divided wall distillation

Energy-saving plant-wide design and plant-wide control of an acetic acid dehydration system with the feed containing methyl acetate and p-xylene are investigated in the study. A heterogeneous azeotropic distillation using isobutyl acetate as an entrainer is designed to obtain high-purity acetic acid at the column bottom and to keep a small acetic acid loss through the top aqueous draw. The accumulation of p-xylene in the column is avoided by adding a side product stream. The mixture in the aqueous phase of decanter, containing mostly water, methyl acetate, and isobutyl acetate is separated using a divided wall distillation column. The whole acetic acid dehydration system includes a heterogeneous azeotropic distillation column and a divided wall distillation column.The control strategies using temperature loops are proposed for this acetic acid dehydration system. For the heterogeneous azeotropic distillation column, the requirements for acetic acid compositions in both the aqueous phase of the decanter and the column bottom can be satisfied by designing entrainer inventory temperature control and cascade temperature control simultaneously. The stages of controlled temperatures are chosen by singular value decomposition and closed-loop analysis methods based on the criteria of minimum entrainer makeup. For the divided wall distillation column, steady-state analysis methods are used for the selection of proper controlled and manipulated variables and the determination of their pairings. Dynamic simulation results demonstrate that the proposed plant-wide control strategy can maintain product purities and reject external disturbances in feed flow and composition changes as well as internal disturbances such as changes in liquid and vapor splits.     

Forecasting Hourly Intermittent Rainfall by Deep Belief Networks with Simple Exponential Smoothing

Accurate rainfall forecasting is essential in planning and managing water resource systems efficiently. However, intermittent rainfall patterns increase the difficulty of accurately forecasting rainfall values. Deep learning techniques have recently been popular and powerful in forecasting. Thus, this study employed deep belief networks with a simple exponential smoothing procedure (DBNSES) to forecast hourly intermittent rainfall values in Taiwan. Weather factors were used as independent variables to forecast rainfall volume. The simple exponential smoothing data preprocessing procedure was used to deal with the intermittent data patterns. The other three forecasting models, namely the least squares support vector regression (LSSVR), the generalized regression neural network (GRNN), and the backpropagation neural network (BPNN), were employed to forecast rainfall using the same data sets. In addition, genetic algorithms were utilized to determine the parameters of four forecasting models. The empirical results indicate that the developed DBNSES models are superior to the other forecasting models in terms of forecasting accuracy. In addition, the DBNSES can obtain smaller values of RMSE than those in the previous studies. Therefore, the DBNSES model is a suitable and effective way of forecasting rainfall with intermittent data patterns.

     

Phase transformation and self-assembly behavior of supramolecular rod–comb block copolymers

We investigated the self-assembly behavior of a series of supramolecular rod–comb block copolymer complexes made by the hybridization of rod–coil diblock copolymers of poly (2,5-di(2-ethylhexyloxy)-1,4-phenylene vinylene)-b-poly(2-vinyl pyridine) (PPV-b-P2VP_f) with different volume fractions, f, of the P2VP coils and an anionic surfactant, dodecyl benzenesulfonic acid (DBSA), that selectively interacts with the P2VP to form the side-chain comb teeth. The resulting hybrids show hierarchically ordered structures at multiple length scales, forming so-called structure-in-structure morphology. Notably, for PPV-b-P2VP_0.56(DBSA)_x, the larger-scale structure changes from a lamellar phase, to a broken lamella, and eventually to a hexagonally packed strip phase with increasing DBSA molar ratio (x) to P2VP monomer unit. Furthermore, simultaneous SAXS and WAXS measurements showed that the order-disorder transition temperatures of larger-scale structures in the PPV-P2VP_0.56(DBSA) rod–comb systems were higher than those associated with the pristine PPV-P2VP_0.56 polymers. The large-scale structure of PPV-P2VP_0.56(DBSA) exists at temperatures around 210 °C even though the rod–rod interaction between PPV blocks disappear at ∼120 °C, signifying that the formation of the P2VP(DBSA) lamellar mesophase plays a critical role in forming the large-scale hexagonally packed strip structures.