The process of continuous bioethanol fermentation often exhibits strong nonlinear dynamics and tends to generate self-oscillatory-state trajectories. The aim of this work is to further investigate such oscillators by coupling with outside periodic forcing, so as to improve process performance. To that end, several strategies for analyzing periodically forced self-oscillatory processes are proposed. By periodically and deliberately forcing an operational parameter, the behavior of the forced fermentor is investigated using limit cycle bifurcation analysis. Specifically, periodic doubling and Neimark–Sacker bifurcations are investigated with limit cycle continuation diagrams. The limit cycle bifurcation analysis is compared with the simulation-based methods, such as stroboscopic and maximum bifurcation maps, and it is shown to be an efficient analysis tool for the periodically forced self-oscillatory system. Process performance enhancement by designing the fermentor to be self-oscillatory is also discussed, as well as the possibility of controlling the oscillatory behavior using external periodic forcing.

The separation of aromatics and aliphatic hydrocarbons is one of the most challenging and energy consuming operations in the petrochemical industry. In this study, two ionic liquids (ILs), N-benzyl-N-methylimidazoium bis(trifluoromethylsulfonyl)imide (IL-a) and N-benzyl-N-vinylimidazolium bis(trifluoromethylsulfonyl)imide (IL-b), were synthesized. Six ternary systems, i.e., toluene–heptane–IL (IL-a or IL-b), benzene–hexane–IL (IL-a or IL-b), and benzene–cyclohexane–IL (IL-a or IL-b), were studied in terms of both quantum chemical calculation and liquid–liquid extraction (LLE). The quantum calculation results showed that both ILs had stronger interaction with aromatics than that with alkanes. For both ILs, stronger binding energy was obtained with toluene than that with benzene. The liquid–liquid extraction experiments were conducted at 298.2 K and atmospheric pressure. The distribution coefficients of aromatic compounds (benzene and toluene) were over 0.72 when IL-a was used as extractant and above 0.75 when IL-b was used. When IL-a was used in the ternary system with benzene and hexane, the selectivity was more than 30, and the distribution coefficient of benzene was over 2.2. Both ILs could be reused more than 10 times without significant loss of selectivity and decrease of distribution coefficients. Better separation performance was obtained at 298.2 K than that at 318.2 K. Another three ternary systems with long-chain alkanes were also tested. Experimental LLE data of these studied ternary systems could be correlated adequately using the NRTL thermodynamic model.

Morpholinium-based ionic liquids (MILs) have attracted increasing interest because of their good extraction performance and low toxicity. In this study, two MILs, N-benzyl-N-methylmorpholinium bis(trifluoromethylsulfonyl)imide (MIL-a) and N-allyl-N-methylmorpholinium bis(trifluoromethylsulfonyl)imide (MIL-b), were synthesized. Six ternary systems, toluene–heptane–IL (MIL-a or MIL-b), benzene–hexane–MIL (IL-a or MIL-b) and benzene–cyclohexane–IL (IL-a or MIL-b), were studied in terms of both quantum chemical calculation and liquid–liquid extraction. The calculation results showed that both MILs had stronger interaction with aromatics than with alkanes. For both cations of MILs, stronger binding energy was obtained with toluene than with benzene. The difference between MIL-a-toluene and MIL-a-cyclohexane was larger than that of MIL-b. As a result, MIL-a showed higher selectivity on toluene than MIL-b. In other ternary systems, the interaction difference was larger than that between MIL-a-benzene and MIL-a-alkanes, which led to a better selectivity of benzene on MIL-b. The liquid–liquid extraction experiment was conducted at 298.2 K and atmospheric pressure. The distribution coefficients of aromatic compounds (benzene and toluene) were over 0.60 when MIL-a was used as extractant, and above 0.50 when MIL-b was used. The selectivity was more than 80, and the distribution coefficient of toluene was over 1.4, when MIL-b was used in the ternary system with benzene and hexane. Both MILs could be reused without significant loss of selectivity and distribution coefficients.

•A PCA model with non-parametric control limit is derived in this paper.•Results show that this method can increase true prediction rate significantly.It is noted that the data for ozone precursors and meteorological variables exhibit non-normal distributions. Thus, to detect surface ozone exceedance days, we propose a principal component analysis (PCA) model with a non-parametric T2 control chart. The input variables include concentrations of ozone (O3), nitrogen oxide (NO), and nitrogen dioxide (NO2), wind speed (WS), relative humidity (RH), solar radiation (SR), surface and aloft temperatures (T). In addition, process variation indicators of meteorological factors are proposed to capture dynamic weather patterns. Ozone precursors and meteorological measurements of non-exceedance days during extended summers (May 16th–Oct. 15th) from 2000 to 2007, which include 1153 days, are used to train a PCA model for the Livermore Valley, CA. Data of ozone exceedance days for the same period are used for validation of the model. Summer data from 2008 to 2009, which include 11 exceedance days, are used to test this prediction model. An ozone exceedance day is triggered when any T2 value of this day exceeds the non-parametric T2 control limit. Compared to a conventional PCA with a Hotelling T2 control chart, the true prediction rate (TPR) of ozone exceedance days using this PCA model with a non-parametric T2 control chart is increased from 45.45% to 72.73%. When process variation indicators (PVIs) are introduced into the PCA model with a non-parametric T2 control chart, the TPR of ozone exceedance days is shown to increase to 100%.

Experimental study has been carried out to investigate the mass transfer behavior when carbon dioxide (CO2) is absorbed through microporous membranes with different porosities. Deionized water and sodium hydroxide (NaOH) aqueous solutions are chosen as absorbents. The effects of membrane porosity, absorbent pH value, and liquid velocity on mass transfer are studied. The effect of membrane porosity on mass transfer depends on both the absorbent pH value and liquid velocity. In the case of low pH values (7−11) in absorbent, membrane porosity almost has no effect on mass transfer at relatively lower liquid velocity, namely, the mass transfer coefficients based on the whole membrane area remain almost the same at different membrane porosities; the effect of membrane porosity on mass transfer becomes obvious with the increasing liquid velocity. Contrarily, as the absorbent pH value increases to 12 or 13, the influence of porosity on mass transfer coefficient becomes significant at varied liquid velocity. The modified mass transfer correlations are obtained with consideration of the effect of porosity over a varying membrane porosity range.

Rectisol process is more efficient in comparison with other physical or chemical absorption methods for gas purification. To implement a real time simulation of Rectisol process, thermodynamic model and simulation strategy are needed. In this paper, a method of modified statistical associated fluid theory with perturbation theory is used to predict thermodynamic behavior of process. As Rectisol process is a highly heat-integrated process with many loops, a method of equation oriented strategy, sequential quadratic programming, is used as the solver and the process converges perfectly. Then analyses are conducted with this simulator.Rectisol process is promising absorption methods for gas purification. A method of modified statistical associated fluid theory with perturbation theory (PC-SAFT) is used to predict thermodynamic behavior of process. As Rectisol process is a highly heat-integrated process with many loops, a method of equation oriented strategy (EO), with sequential quadratic programming (SQP) method, is used as the solver and the process converges perfectly.Download full-size image

•ALCA analysis was used to evaluate environmental impacts of a starch WWTP in China.•The ALCA results were compared against a conventional WWTP.•CLCA analysis was used to evaluate the effects of renewable biomass energy usage.•Global and regional normalization factors were used to interpret the LCA results.•This LCA study also identified the potential of sustainable renewable energy usage.A life cycle assessment (LCA) analysis was carried out to evaluate the environmental performance related to a corn starch wastewater treatment plant (WWTP) with simultaneous microbial oil production in Shandong, China, compared against a non-oil producing WWTP. The software GaBi 5.43 was employed for the LCA analysis. Applying an attributional modeling LCA the results showed that the WWTP, despite removing high concentrations of organic matter from the wastewater and being economically feasible by the production of crude bio-oil, has 2330% increased emissions related to energy consumption into the air compared to a non-oil production process. Taking in consideration an estimated activated sludge WWT and anaerobic digester process, the conventional process would have higher GHG emissions. With the LCA results, a consequential modeling LCA taking corn stover biomass as renewable energy source in a direct-fire system was proposed. It showed that corn stover biomass has the potential to mitigate the high emissions to the air due to the abundant available resources near the plant location. Global and regional normalization references were also used to represent the real impact of the LCA results. This study not only revealed an environmental evaluation of the current wastewater microbial oil production technology, but it also helped to identify process bottlenecks and the use of renewable energy opportunities which should receive specific research efforts to make this process environmentally sustainable.

Batch processes with multiple phases are commonly found in process industries. Process dynamics and correlations among variables also tend to change with the transitions across such phases. Traditional approaches where the model is constructed from data representing the whole batch process would not be sufficient to capture the varying process dynamics and correlation structure. Different ways of phase segmentation and modeling strategies have been previously reported that account for the multi-phase characteristics of such processes. For a given process dynamic or a particular phase in a multi-phase process, a Principal Component Analysis (PCA) model can project the maximum deviation with small number of principal components, and represent a certain percentage of the deviation with fixed number of principal components. As the process dynamics change, the percentage represented by the fixed number of PCs also changes if a single PCA model is applied. In this paper, a new phase identification method is proposed based on the change of the first cumulative contribution between different PCA models. Every phase is modeled separately based on the phase identification. The method is applied to fault detection in the fed-batch penicillin cultivation process. The results show that the method can better capture the process dynamics in different phases and detect process upset in an early stage.