冷链条件下气调包装酱卤鸭肉制品货架期预测模型的建立

目的 以气调包装酱卤鸭肉制品为研究对象,在冷链温度范围内建立一套准确、高效的货架期预测模型。方法 利用选择性培养基测定不同温度下产品各微生物数量,确定4~25℃条件下产品优势腐败菌。对乳酸菌数量与感官评定值进行了回归分析确定最小腐败量Ns。分别采用修正的Gompertz方程和平方根方程建立一、二级模型,并通过预测值与实测值对比验证模型的可靠性。结果 确定了4~25℃条件下产品优势腐败菌为乳酸菌,最小腐败量Ns=6.14(lg(cfu /g))。一、二级模型拟合度均良好,三种温度下模型预测值与实际值间的差异均在30％左右,波动幅度在10%以内。结论 实现了对4~25℃内任何时间点产品剩余货架期的预测,为冷链条件下气调包装酱卤鸭肉制品品质的变化提供了理论指导。     

Design and synthesis of side-chain optimized poly(2,6-dimethyl-1,4-phenylene oxide)-g-poly(styrene sulfonic acid) as proton exchange membrane for fuel cell applications: Balancing the water-resistance and the sulfonation degree

Side-chain optimized poly (2,6-dimethyl-1,4-phenylene oxide)-g-poly (styrene sulfonic acid) (PPO-g-PSSA) is designed with balanced water-resistance and sulfonation degree. The PPO-g-PSSA is synthesized by controlled atom-transfer radical polymerization (ATRP) from brominated poly (2,6-dimethyl-1,4-phenylene oxide) (PPO-xBr) and ethyl styrene-4-sulfonate and followed by hydrolysis. A series of PPO-g-PSSA are prepared possessing different bromination degree (x) of PPO-xBr and polymerization degree (m) of the side-chains and the water-resistances of the fabricated membranes are investigated. The results show that a PPO-g-PSSA at relatively low x (x < 0.2) and high m (m > 4) exhibits good balance between the water-resistance and the sulfonation degree. Namely, it displays suitable proton conductivity with compromised water-resistance. Moreover, a maximum ion exchange capacity (IEC) of 3.24 mmol g^?1 is reached without the sacrifice of water-resistance. In addition, PPO-g-0.08PSSA-13 and PPO-g-0.14PSSA-4 are chosen characterized by thermogravimetric analysis, proton conductivities and mechanical properties. At 90% RH, the optimized PPO-g-0.08PPSA-13 possesses a proton conductivity of 37.9 mS cm^?1 at 40 °C and 45.5 mS cm^?1 at 95 °C, respectively.     

Investigation on sulphonated zinc oxide nanorod incorporated sulphonated poly (1,4-phenylene ether ether sulfone) nanocomposite membranes for improved performance of microbial fuel cell

Hydrothermally prepared zinc oxide nanorods are sulphonated (S–ZnO NR) and incorporated into 15% Sulphonated Poly (1,4-Phenylene Ether Ether Sulfone) (SPEES) to improve the hydrophilicity, water uptake and ion transfer capacity. Water uptake and ion transfer capacity increased to 34.6 ± 0.6% and 2.0 ± 0.05 meq g^?1 from 29.8 ± 0.3% and 1.4 ± 0.04 meq g^?1 by adding 7.5 wt% S–ZnO NR to SPEES. Morphological studies show the prepared S–ZnO NR is well dispersed in the polymer matrix. SPEES +7.5 wt% S–ZnO NR membrane exhibits optimum performance after three-weeks of continual operation in a fabricated microbial fuel cell (MFC) to produce a maximum power density of 142 ± 1.2 mW m^?2 with a reduced biofilm compared to plain SPEES (59 ± 0.8 mW m^?2), unsulphonated filler incorporated SPEES (SPEES + 7.5 wt% ZnO, 68 ± 1.1 mW m^?2) and Nafion (130 ± 1.5 mW m^?2) thereby suggesting its suitability as a sustainable and improved cation exchange membrane (CEM) for MFCs.     

Mixture ratio modeling of dynamic systems

Any knowledge extraction relies (possibly implicitly) on a hypothesis about the modelled-data dependence. The extracted knowledge ultimately serves to a decision-making (DM). DM always faces uncertainty and this makes probabilistic modelling adequate. The inspected black-box modeling deals with “universal” approximators of the relevant probabilistic model. Finite mixtures with components in the exponential family are often exploited. Their attractiveness stems from their flexibility, the cluster interpretability of components and the existence of algorithms for processing high-dimensional data streams. They are even used in dynamic cases with mutually dependent data records while regression and auto-regression mixture components serve to the dependence modeling. These dynamic models, however, mostly assume data-independent component weights, that is, memoryless transitions between dynamic mixture components. Such mixtures are not universal approximators of dynamic probabilistic models. Formally, this follows from the fact that the set of finite probabilistic mixtures is not closed with respect to the conditioning, which is the key estimation and predictive operation. The paper overcomes this drawback by using ratios of finite mixtures as universally approximating dynamic parametric models. The paper motivates them, elaborates their approximate Bayesian recursive estimation and reveals their application potential.     

Flow state monitoring of gas-water two-phase flow using multi-Gaussian mixture model based on canonical variate analysis

As a highly complex and time-varying process, gas-water two-phase flow is commonly encountered in industries. It has a variety of typical flow states and transition flow states. Accurate identification and monitoring of flow states is not only beneficial to further study of two-phase flow but also helpful for stable operation and economic efficiency of process industry. Combining canonical variate analysis (CVA) and Gaussian mixture model (GMM), a strategy called multi-CVA-GMM is proposed for flow state monitoring in gas-water two-phase flow. CVA is used to extract flow state features from the perspective of correlation between historical data and future data, which solves the cross correlation and temporal correlation of multi-sensor measurement data. GMM calculates the possibility that the current flow state belongs to each typical flow pattern and judges the current flow state by probability indicators. It is conducive to follow-up use of Bayesian inference probability and Mahalanobis distance-based (BID) indicator for flow state monitoring, which avoids repeated traversal of multiple CVA-GMM models and improves the efficiency of the monitoring process. The probability indicators can also be used to analyze transition flow states. The method combining the probabilistic idea of GMM with the deterministic idea of multimodal modeling can accurately identify the current flow state and effectively monitor the evolution of flow state. The multi-CVA-GMM method is validated by using the measured data of the horizontal flow loop of gas-water two-phase flow experimental facility, and its effectiveness is proved.     

Fabrication of BSCF-based mixed ionic-electronic conducting membrane by electrophoretic deposition for oxygen separation application

Ba_0.5Sr_0.5Co_0.8Fe_0.2O_3-δ (BSCF), which exhibits a high mixed oxide ionic-electronic conduction, was used for the fabrication of an oxygen separation membrane. An asymmetric structure, which was a thin and dense BSCF membrane layer supported on a porous BSCF substrate, was fabricated by the electrophoretic deposition method (EPD). Porous BSCF supports were prepared by the uniaxial pressing method using a powder mixture with BSCF and starch as the pore-forming agent (0–50 wt.%). The sintering behaviors of the porous support and the thin layer were separately characterized by dilatometry to determine the co-fired temperature at which cracking did not occur. A crack-free and thin dense membrane layer, which had about a 15 μm thickness and >95% relative density, was obtained after optimizing the processes of EPD and sintering. The dense/porous interface was well-bonded and the oxygen permeation flux was 2.5 ml (STP) min⁻¹ cm^-2 at 850 °C.     

Computational modelling of water flow inside laboratory Knelson concentrator bowl

Enhanced gravity concentrators such as Knelson concentrator (KC) are extensively used in the mineral processing industry. The complexities of KC bowl geometry and variation of feed characteristics have forced process engineers to design empirically new units using laboratory and pilot-scale Knelson concentrators. However, numerical modelling methods such as computational fluid dynamics (CFD) and discrete element method (DEM) provide a better insight of flow behaviour of fluid and particulate solid phases inside these processing units. This article reports findings of CFD simulations for single-phase water flow inside the laboratory KC. An available standard 7.5-cm laboratory KC bowl was numerically simulated using realisable k-ε turbulence model to resolve the turbulence dispersion of existing transitional flow regime. The effects of relative centrifugal force (RCF) intensity and bed fluidisation water flow rate on the water velocity and pressure distributions were studied. Simulations confirmed the swirling flow pattern governing inside the bowl. The results revealed that the impact of RCF intensity on the water field values is greater than that of bed fluidisation water flow rate. Both velocity and pressure variations inside the bowl rings followed a linear trend.     

A knowledge construction methodology to automate case-based learning using clinical documents

The case-based learning (CBL) approach has gained attention in medical education as an alternative to traditional learning methodology. However, current CBL systems do not facilitate and provide computer-based domain knowledge to medical students for solving real-world clinical cases during CBL practice. To automate CBL, clinical documents are beneficial for constructing domain knowledge. In the literature, most systems and methodologies require a knowledge engineer to construct machine-readable knowledge. Keeping in view these facts, we present a knowledge construction methodology (KCM-CD) to construct domain knowledge ontology (i.e., structured declarative knowledge) from unstructured text in a systematic way using artificial intelligence techniques, with minimum intervention from a knowledge engineer. To utilize the strength of humans and computers, and to realize the KCM-CD methodology, an interactive case-based learning system(iCBLS) was developed. Finally, the developed ontological model was evaluated to evaluate the quality of domain knowledge in terms of coherence measure. The results showed that the overall domain model has positive coherence values, indicating that all words in each branch of the domain ontology are correlated with each other and the quality of the developed model is acceptable.     

Development and validation of a micro-fin tubes evaporator model using R134a and R1234yf as working fluids

This paper presents a model of shell and tube evaporator with micro-fin tubes using R1234yf and R134a. The model developed for this evaporator uses the ε-NTU method to predict the evaporating pressure, the refrigerant outlet enthalpy and the outlet temperature of the secondary fluid. The model accuracy is evaluated using different two-phase flow boiling correlations for micro-fin tubes and comparing predicted and experimental data. The experimental tests were carried out for a wide range of operating conditions using R134a and R1234yf as working fluids. The predicted parameter with maximum deviations, between the predicted and experimental data, is the evaporating pressure. The correlation of Akhavan– Behabadi et al. was used to predict flow boiling heat transfer, with an error on cooling capacity prediction below 5%. Simulations, carried out with this validated model, show that the overall heat transfer coefficient of R1234yf has a maximum decrease of 10% compared with R134a.