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Abstract In this study, crude cellulose derived from cornstalk, after bleaching, was used as raw material for the synthesis of sodium carboxymethyl cellulose (CMC) by reacting with the cellulose with NaOH and chloroacetic acid at 75 °C for 1.5 h. Effects of alkali dosage, concentration of chloroacetic acid on the physical and chemical properties of the CMC products were investigated. It was revealed that the reactants alkali reagent/chloroacetic acid/cellulose at the molar ratio of 4.6:2.8:1and 4:2.5:1, or at the molar ratio of NaOH/ClCH 2 COOH ≈1.6–1.64, resulted in CMC products of relatively high water solubility. The viscosity-average molecular weight M v of these two CMC products obtained at molar ratios of 4.0:2.5:1 and 4.6:2.8:1 is in the range of 1.94 × 10 4 –2.48 × 10 4 g mol −1 , and the average DS of the two products are 0.57 and 0.85, respectively. As the solute concentration is above 2 wt%, the viscosity of the CMC-water solution exhibits nonlinear (exponential) increasing with increasing the solute concentration (typical of non-Newton fluids).

Energy management is an enabling technique to guarantee the reliability and economy of hybrid electric systems. This paper proposes a novel machine learning-based energy management strategy for a hybrid electric bus (HEB), with an emphasized consciousness of both thermal safety and degradation of the onboard lithium-ion battery (LIB) system. Firstly, the deep deterministic policy gradient (DDPG) algorithm is combined with an expert-assistance system, for the first time, to enhance the “cold start” performance and optimize the power allocation of HEB. Secondly, in the framework of the proposed algorithm, the penalties to over-temperature and LIB degradation are embedded to improve the management quality in terms of the thermal safety enforcement and overall driving cost reduction. The proposed strategy is tested under different road missions to validate its superiority over state-of-the-art techniques in terms of training efficiency and optimization performance.

In recent years, power quality (PQ) disturbance data are increasingly applied to extract useful information about the condition of power systems, such as monitoring incipient equipment failures. A prerequisite for such applications is the ability for a PQ monitor to detect abnormal waveforms. In response to this need, a generic method for waveform abnormality detection is proposed in this paper. The proposed method has two unique features. First, abnormalities are detected by comparing the statistical distributions of waveform variations with and without disturbances. Kullback-Leibler divergence (KLD) is used to assess the difference of the distributions. An abnormality exists if the KLD is larger than a threshold. Second, current waveforms are used for detection since they are more sensitive to equipment conditions. The difficulty to set a proper threshold due to large variations of current values is overcome through the adoption of KLD as the distance measure and a systematic threshold selection scheme. The scheme maximizes the detection probability for a given false alarm probability. Field-measured data and simulated data are applied to verify the effectiveness of the method.

Abstract Bitumen recovery by steam-solvent coinjection involves the coupled thermal/compositional mechanisms for reduction of bitumen viscosity. Reliable design of such processes requires reservoir flow simulation based on a proper phase-behavior model so that the oleic-phase viscosity near the steam-chamber edge can be modeled reliably. However, the effect of bitumen characterization (e.g., the number of pseudo components used) on steam-solvent coinjection simulation has not been studied in detail, and can be realized only after running multiple reservoir simulations, which is time consuming. There are two main objectives in this paper. One is to develop a reliable method for bitumen characterization by improving the fluid characterization method that was recently developed based on perturbation from n-alkanes (PnA). The other is to develop a novel analytical method for assessing the sensitivity of a particular coinjection simulation to bitumen characterization without having to perform reservoir simulations. A simulation case study is given to validate this analytical method. A proper number of pseudo components for bitumen characterization cannot be determined without considering the effect of phase behavior on the oleic-phase viscosity at chamber-edge conditions in steam-solvent coinjection simulation. Results show that the analytical method developed in this research can detect the sensitivity of recovery simulation to bitumen characterization without performing multiple flow simulations using different sets of fluid models. The PnA-based method developed for bitumen characterization gives reliable predictions of phase behavior for bitumen/solvent mixtures with a small amount of experimental data.

Abstract Homogeneous charge compression ignition (HCCI) combustion is a spontaneous multi-site auto-ignition of a lean premixed fuel–air mixture, which has high heat release rate, short combustion duration and no evidence of flame propagation. In HCCI engines, there is no direct control method for the time of auto-ignition. Auto-ignition timing should be controlled in order to make heat release process take place at the appropriate time in the engine cycle. Heat release analysis is a diagnostic tool which aids engine experimenters. It facilitates the endeavors being conducted in obtaining a control method by investigating heat release rate and also cumulative heat release. This study can be divided into two parts. First, traditional first law heat release model which is widely used in engine combustion analysis was presented and the applicability of this model in HCCI engines was investigated. Second, a new heat release model based on the first law of thermodynamics accompanying with a temperature solver was developed and assessed. The model was applied in four test conditions with different operating conditions and a variety of fuel compositions, including i -octane, n -heptane, pure NG, and at last, a dual fueled case of NG and n -heptane. Results of this work indicate that utilizing the modified first law heat release model together with a solver for temperature correction will guarantee obtaining a well-behaved and accurate apparent heat release trend and magnitude in HCCI combustion engines.

Abstract Morphological characteristics of poplar and willow clones were determined in order to identify main characteristics leading to superior growth under increased plant competition with low or high nitrogen (N) availability. Seven hybrid poplar ( Populus spp. including one hybrid aspen) and five willow ( Salix spp.) clones were grown under greenhouse conditions for 13 weeks at three spacings (20 × 20, 35 × 35, and 60 × 60 cm) and two N levels (20 and 200 mg kg −1 ). The decrease in spacing from 60 to 20 cm reduced leaf area by 50% but clones had similar aboveground biomass per tree under all spacings, with increasing their height per unit leaf area. More productive clones had greater leaf area (+102%), leaf area per unit plant biomass (+12%) and lower root-to-shoot ratios (−27%) compared to less productive clones. There were positive relationships between leaf area and above-ground biomass per tree for both more and less productive clones. Compared to low N level and 60 cm spacing, trees growing in high N level and 20 cm spacing reached similar root collar diameter, crown width, and leaf area values and even greater height, suggesting that an addition of N could help mitigate negative effects of tree competition.

Modal transformation decomposes three-phase power system voltages or currents into three sets of de-coupled quantities. The symmetrical components transformation is a well-known example of modal transformation. In this paper, the applications and performances of three different modal transformations for determining power system harmonic impedances are investigated. The results show that the symmetrical components transformation is not the best candidate for harmonic impedance calculation. The Clarke and 012 transformations can provide more reliable results. Computer simulation and field measurement results are used to demonstrate the pros and cons of different transformation methods.

Abstract This study employs a parametric input distance function that incorporates both desirable and undesirable outputs to provide a more complete representation of the production technology from which environmentally sensitive productivity and efficiency measures can be generated. This framework also generates pollution abatement cost estimates that are useful for policy making. An input-based Malmquist index of productivity growth that appropriately credits the producer not only for increases in marketable or desirable outputs but also for the production of improved environmental quality through pollution abatement activities is derived from the input distance function. The method was applied to time series data from the Canadian pulp and paper industry. Our shadow price estimates indicate that the marginal cost to producers of pollution control has been rising. The main conclusion of this study is that productivity improvement, from the social viewpoint, has been stronger than conventional measures would suggest.

This paper presents a probabilistic harmonic simulation method to study the power-quality impact of electric vehicles (EVs). The proposed method takes into account the random operating characteristics of the vehicles, such as random charging time, charging duration, and vehicle locations. A multiphase model of the primary and secondary systems is used to represent the study system. Extensive case studies have been conducted using the method and the measured EV data. The results reveal that the current versions of plug-in hybrid EVs have no significant harmonic impact on power systems. However, the Level 1 charger can increase the neutral-to-earth voltage at homes which could lead to increased stray voltage incidents.

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