Integrating product and process design with optimal control: A case study of solvent recycling

This paper proposes the simultaneous integration of environmentally benign solvent selection (product design), solvent recycling (process design) and optimal control for the separation of azeotropic systems using batch distillation. The previous work performed by Kim et al. (2004. Entrainer selection and solvent recycling in complex batch distillation. Chemical Engineering Communications 191(12), 1606-1633) combines the chemical synthesis and process synthesis under uncertainty. For batch distillation, optimal operation is also important due to the unsteady state nature of the process and high operating costs. Optimal control allows us to optimize the column operating policy by selecting a trajectory for the reflux ratio. However, there are time-dependent uncertainties in thermodynamic models of batch distillation due to the assumption of constant relative volatility. In this paper, the uncertainties in relative volatility were modeled using Ito processes and the stochastic optimal control problem was solved by combined maximum principle and non-linear programming (NLP) techniques. Then the previous work of optimal solvent selection and recycling was coupled with optimal control. As a real world example for this integrated approach, a waste stream containing acetonitrile-water was studied. The optimal design parameters obtained by Kim et al. (2004. Entrainer selection and solvent recycling in complex batch distillation. Chemical Engineering Communications 191(12), 1606-1633), for this separation were used and the optimal control policy is computed first without considering uncertainties by variable transformation technique. The deterministic optimal control policy improves the product yield by 4.0% as compared to the base case, verified using a rigorous simulator for batch distillation. When the stochastic optimal control policy was computed representing the relative volatility as an Ito process, a similar recovery rate was obtained from simulations, but the batch time was reduced significantly, producing the most profitable operation.     

Microwave irradiation of guar seed flour: Effect on anti-nutritional factors,phytochemicals, in vitro protein digestibility,thermo-pasting,structural, and functional attributes

Guar seed flour (GSF) has a high amount of carbohydrates, proteins, phytochemicals, and anti-nutritional factors (ANFs), which limits its use. To address this issue, the current study was undertaken to understand the effect of microwave (MW) irradiation on ANFs, phytochemicals, in vitro protein digestibility (IVPD), and functional attributes of GSF at varying power density (P_d: 1–3 W/g) and duration (3–9 min). The ANFs were determined using a colorimetric assay and a Fourier transform infrared spectrum. At 3 P_d-9 min, the maximum reduction in ANFs (tannin, phytic acid, saponin, and trypsin inhibitor activity) was observed. Higher P_d and treatment duration increased antioxidant activity and total phenolic content, except for total flavonoid content. Furthermore, compared to the control sample (78.38%), the IVPD of the GSF samples increased to 3.28% (3 P_d-9 min). An increase in P_d and duration of MW treatment improved the thermal and pasting properties of GSF samples up to 2 P_d-9 min. Due to inter- and intramolecular hydrogen bonding degradation, the relative crystallinity of the 3 P_d-9 min treated GSF sample was 30.58%, which was lower than that of the control (40.08%). In MW-treated samples, SEM images revealed smaller clusters with rough and porous structures. However, no noticeable color (ΔE) changes were observed in MW-treated samples. Aside from water absorption capacity and water solubility index, MW treatment reduced oil absorption capacity, foaming capacity, and emulsifying capacity. As demonstrated by principal component analysis, MW irradiation with moderate P_d (2–3) was more effective in reducing ANFs, retaining nutritional contents, and improving the digestible properties of GSF, which could be a potential ingredient for developing gluten-free products.     

Comparative studies on physicochemical and baking properties of newly harvested and stored indian varieties of wheat

Comparative studies on the progressive changes in distribution pattern of proteins, and in rheological and baking properties, were carried out with seven newly harvested and stored wheat varieties. Freshly harvested wheat contained a larger amount of low molecular weight gliadins, which were aggregated during storage in air. This resulted in improvement of the rheological and baking properties of stored grains. Thus, gluten content in wheat or, more precisely, the interchange reactions between thiol and disulphide during storage, governed the dough rheology. The same effects could be achieved by addition of an oxidising agent such as KBrO₃. Radiation treatment (up to 200 krad) also improved the baking quality of newly harvested wheat by modifying some of its rheological properties. A significant correlation between SS/SH ratio and loaf volume was observed. Lower loaf volume in newly harvested wheat was associated with its low SS/SH ratio, high maximum gelatinisation viscosity and low gas retention capacity.     

Soluble iron modulates iron oxide particle-induced inflammatory responses via prostaglandin E2 synthesis: In vitro and in vivo studies

Background  

Ambient particulate matter (PM)-associated metals have been shown to play an important role in cardiopulmonary health outcomes. To study the modulation of PM-induced inflammation by leached off metals, we investigated intracellular solubility of radio-labeled iron oxide (⁵⁹Fe₂O₃) particles of 0.5 and 1.5 μm geometric mean diameter. Fe₂O₃ particles were examined for the induction of the release of interleukin 6 (IL-6) as pro-inflammatory and prostaglandin E₂ (PGE₂) as anti-inflammatory markers in cultured alveolar macrophages (AM) from Wistar Kyoto (WKY) rats. In addition, we exposed male WKY rats to monodispersed Fe₂O₃ particles by intratracheal instillation (1.3 or 4.0 mg/kg body weight) to examine in vivo inflammation.     

Numerical Investigation of Sheath and Aerosol Flows in the Flow Combination Section of a Baron Fiber Classifier

The Baron fiber classifier is an instrument used to separate fibers by length. The flow combination section (FCS) of this instrument is an upstream annular region, where an aerosol of uncharged fibers is introduced along with two sheath flows; length separation occurs by dielectrophoresis downstream in the flow classification section. In its current implementation at NIOSH, the instrument is capable of processing only very small quantities of fibers. In order to prepare large quantities of length-separated fibers for toxicological studies, the throughput of the instrument needs to be increased, and hence, higher aerosol flow rates need to be considered. However, higher aerosol flow rates may give rise to flow separation or vortex formation in the FCS, arising from the sudden expansion of the aerosol at the inlet nozzle. The goal of the present investigation is to understand the interaction of the sheath and aerosol flows inside the FCS, using computational fluid dynamics (CFD), and to identify possible limits to increasing aerosol flow rates. Numerical solutions are obtained using an axisymmetric model of the FCS, and solving the Navier–Stokes equations governing these flows; in this study, the aerosol flow is treated purely aerodynamically. Results of computations are presented for four different flow rates. The geometry of the converging outer cylinder, along with the two sheath flows, is effective in preventing vortex formation in the FCS for aerosol-to-sheath flow inlet velocity ratios below ～50. For higher aerosol flow rates, recirculation is observed in both inner and outer sheaths. Results for velocity, streamlines, and shear stress are presented.

Copyright 2014 Prahit Dubey, Urmila Ghia, and Leonid A. Turkevich     

Heterogeneous multi-agent optimization framework with application to synthesizing optimal nuclear waste blends

Multi-agent optimization method is a nature-inspired framework that supports the cooperative search of an optimal solution of an optimization problem by a group of algorithmic agents connected through an environment with certain predefined information sharing protocol. In this work, we propose a novel heterogeneous multi-agent optimization (HTMAO) framework. The proposed framework is validated using a set of benchmark problems a real-world synthesizing radioactive waste blending problem. The optimal radioactive waste blending problem is formulated as a mixed integer nonlinear programming. The total frit used for vitrification process is minimized subject to thermodynamic properties and process model constraints. The model simultaneously determines the optimal decisions that include the combination of the waste tanks that form each waste blend and the amount of frit needed for the vitrification of each waste blend. In developing the HTMAO framework, efficient ant colony optimization algorithms; efficient simulated annealing; efficient genetic algorithm; and sequential quadratic programming solver are considered as algorithmic agents. We illustrate this approach through a real-world case study of the optimal radioactive waste blending of Hanford site in Southern Washington where nuclear waste is stored.     

Hammersley stochastic annealing: efficiency improvement for combinatorial optimization under uncertainty

This paper presents hierarchical improvements to combinatorial stochastic annealing algorithms using a new and efficient sampling technique. The Hammersley Sequence Sampling (HSS) technique is used for updating discrete combinations, reducing the Markov chain length, determining the number of samples automatically, and embedding better confidence intervals of the samples. The improved algorithm, Hammersley stochastic annealing, can significantly improve computational efficiency over traditional stochastic programming methods. This new method can be a useful tool for large-scale combinatorial stochastic programming problems. A real-world case study involving solvent selection under uncertainty illustrates the usefulness of this new algorithm.     

Real option theory from finance to batch distillation

Batch distillation processes have gained renewed interest because of the recent development in small-scale industries producing high-value-added, low-volume specialty chemicals. The flexibility and unsteady state nature of batch distillation constitute a challenge for the designer. A particularly difficult problem is the optimal control problem involving open loop solution for the reflux ratio profile. This is because of the complexity of the formulation and the large computational effort associated to its solution. The mathematical and numerical complexities of the optimal control problem get worse when uncertainty is present in the formulation. In this work, by applying the optimality conditions from the real option theory based on the Ito's Lemma [Investment under uncertainity (1994); Memoirs Am. Math. Soc. 4 (1951) 1; Appl. Math. Opt. 4 (1974) 374], the mathematical tools needed to solve optimal control problems in batch distillation columns when uncertainties in the state variables are present have been developed. Furthermore, the coupled maximum principle and NLP approach developed by Diwekar [Am. Inst. Chem. Eng. J. 38 (1992) 1551] has been extended for solving the optimal control problem in the uncertain case. This new algorithm has been implemented in the MultiBatchDS batch distillation process simulator. Finally, a numerical case-study is presented to show the scope and application of the proposed approach.     

Characterization and stochastic modeling of uncertainties in the biodiesel production

There are inherent uncertainties in the biodiesel production process arising out of feedstock composition, operating and design parameters and can have significant impact on the product quality and process economics. In this paper, the uncertainties are quantified in the form of probabilistic distribution function. Stochastic modeling capability is implemented in the ASPEN process simulator to take into consideration these uncertainties and the output is evaluated to determine impact on process efficiency and quality of biodiesel.