Challenges and Opportunities of Modeling Biomass Gasification in Aspen Plus: A Review

Aspen Plus has become one of the most common process simulation tools for both academia and industrial applications. In the last decade, the number of the papers on Aspen Plus modeling of biomass gasification has significantly increased. This review focuses on recent developments and studies on modeling biomass gasification in Aspen Plus including key aspects such as tar formation and model validation. Accordingly, challenges in modeling due to specific assumptions and limitations will be highlighted to provide a useful basis for researchers and end-users for further process modeling of biomass gasification in Aspen Plus.     

A new MM algorithm for constrained estimation in the proportional hazards model

The constrained estimation in Cox’s model for the right-censored survival data is studied and the asymptotic properties of the constrained estimators are derived by using the Lagrangian method based on Karush–Kuhn–Tucker conditions. A novel minorization–maximization (MM) algorithm is developed for calculating the maximum likelihood estimates of the regression coefficients subject to box or linear inequality restrictions in the proportional hazards model. The first M-step of the proposed MM algorithm is to construct a surrogate function with a diagonal Hessian matrix, which can be reached by utilizing the convexity of the exponential function and the negative logarithm function. The second M-step is to maximize the surrogate function with a diagonal Hessian matrix subject to box constraints, which is equivalent to separately maximizing several one-dimensional concave functions with a lower bound and an upper bound constraint, resulting in an explicit solution via a median function. The ascent property of the proposed MM algorithm under constraints is theoretically justified. Standard error estimation is also presented via a non-parametric bootstrap approach. Simulation studies are performed to compare the estimations with and without constraints. Two real data sets are used to illustrate the proposed methods.     

Efficient model selection for real-time adaptive cold start strategy of a fuel cell system on vehicular applications

The PEMFC maximum power is greatly influenced by subfreezing temperature and degradation phenomena. Therefore, a dependable model is required to estimate the power with respect to the variation of the operating conditions and state of health. Semi-empirical models are potent tools in this regard. Nonetheless, there is not much information about their cold environment reliability. This paper comprehensively compares the performance of some models (already tested in normal ambient temperature) in subfreezing condition to introduce the most reliable one for PEMFC cold start-up application. Firstly, seven models are compared regarding voltage losses and precision. Subsequently, the three most dependable ones are selected and experimentally compared at sub-zero temperature in terms of polarization curve estimation for three PEMFCs with different degradation levels. The results of this study indicate that the model introduced by Amphlett et al. has a superior performance compared to other ones regarding the characteristic's estimation in below-zero temperature.     

Elasto-Thermodiffusive Response in a Spherically Isotropic Hollow Sphere

The present work deals with the investigation of elasto-thermo diffusion interactions inside a spherically isotropic hollow sphere in the context of linear theory of generalized thermoelastic diffusion based on Green and Lindsay theory. The inner and outer boundaries of the body are free from stresses and are subjected to a time-dependent thermal shock and also the chemical shock. Laplace transform techniques are used to write the basic equations in the form of a vector matrix differential equations, which is then solved by the eigenvalue approach. The inversion of the transformed solution is carried out by applying the method of Bellman. The stress, temperature, mass concentration and chemical potential are computed and presented graphically. A comparative study of diffusive medium and thermoelastic medium is carried out, and it was seen that the effect of diffusion is significant on the stresses. A comparison of spherically isotropic body with isotropic body has also been presented, and a significant difference is observed.     

A datamining approach to classify,select and predict the formation enthalpy for intermetallic compound hydrides

In this paper, two techniques of datamining tools were adopted, a principal component analysis (PCA) and artificial neural network (ANN). A PCA to classify, select and identify several combinations between transition element A and B (B = Ti, Zr, Hf, Sc, Y, La and Th) and ANN to predict ΔH for ternary hydrides. Based on the datasets selected from different works, a principal component analysis (PCA) has been applied to select, classify and identify around 76 possible combinations between transition metal elements A and B. The results showed that the clustering of combinations A-B are significantly influenced by the atomic parameters of element A, such atomic radius (R_A), Pauling's electronegativity (χ_A) and atomic electron density (Z_A/R_A³). From 76 combinations, 55 systems which have χ_A ≥ 1.5, Z_A/R_A³>1.28 and R_A < 1.46 Å are categorized as group 1. On the other hand, 21 systems which have χ_A < 1.5, Z_A/R_A³ < 1.28, and R_A > 1.46 Å are categorized as group 2. From the first group, 46 different combinations are identified and have a negative ΔH, within 18 well-known promising binary alloys of hydrogen storage.An (6-15-1) architecture of artificial neural network (ANN) has been developed to estimate the ΔH for the other ternary hydrides selected from different published works. The performance indices such as relative error, coefficient of determination (R²) and mean square error (MSE) were used to control the performance of obtained results. In addition to this, the ΔH obtained from ANN model were compared with those experimental data and theoretical results available in the literature.     

Calculating the degree of degradation of the volatile solids in continuously operated bioreactors

Determining the degree of degradation is an important means of assessing the efficiency of biological processes. However, one should consider the fact that during degradation, the reference value, such as volume or the mass of total solids, is also subject to change. The assumption that the incoming and outgoing mass flows are identical is only possible for substrates with a high water content and hence, a low energy density. For substrates with a higher energy density, a correction by the gaseous mass flow is required, but usually its quantification is difficult, especially when examining full-scale plants or open systems. Based on the assumption that the mass of inorganic solids is constant during the process, a universally applicable equation has been developed, requiring only the input and output volatile solids concentrations for calculation.     

Waste-to-wealth approach in water economy: The case of beneficiation of mercury-contaminated water in hydrogen production

Heavy metals (Hg, Cd, Pb, etc.) are micro-pollutants and result in water contamination. Significant bio-concentration of heavy metal like Hg can lead to fatal disease such as Minamata. Given this context, heavy metal removal from wastewater is essential before discharge. The wastewater treatment process requires considerable amount of energy which is being met by the conventional carbon-based fuels. This contributes to the global carbon dioxide emission, and hence global warming. Therefore, if clean energy sourcing is enabled during the treatment of the wastewater; it would offer obvious advantages. If the energy production is ‘clean’ and achieved via the process itself, it would serve two outcomes: (a) meeting the energy demand for wastewater treatment, and (b) getting rid of the need for external ‘carbon-based’ energy. Recently a few research articles have reported simultaneous clean energy production from wastewater during its treatment. Thus, the energy demand of the wastewater treatment process can be potentially met with the clean energy produced during the process. In this review, we will discuss mercury-contaminated wastewater treatment with simultaneous hydrogen production. We will provide a brief overview of waste-to-wealth approaches currently prevailing in water economy, recent mercury removal processes, and discuss future possibilities of self-sustained Hg-contaminated wastewater treatment.     

Quality improvement and backorder price discount under controllable lead time in an inventory model

The proposed study investigates a continuous review inventory model with order quantity, reorder point, backorder price discount, process quality, and lead time as decision variables. An investment function is used to improve the process quality. Two models are developed based on the probability distribution of lead time demand. The lead time demand follows a normal distribution in the first model and in the second model it does not follow any specific distribution but mean and standard deviation are known. We prove two lemmas to obtain optimal solutions for the normal distribution model and distribution free model. Finally, some numerical examples are given to illustrate the model.     

Effect of different control strategies on rapid cold start-up of a 30-cell proton exchange membrane fuel cell stack

Many places experience extreme temperatures below −30 °C, which is a great challenge for the fuel cell vehicle (FCV). The aim of this study is to optimize the strategy to achieve rapid cold start-up of the 30-cell stack at different temperature conditions. The test shows that the stack rapidly starts within 30 s at an ambient temperature of −20 °C. Turning on the coolant at −25 °C show stability of the cell voltage at both ends due to the end-plate heating, however, voltage of intermediate cells fluctuates sharply, and successful start-up is completed after 60 s. The cold start strategy changes to load-voltage cooperative control mode when the ambient temperature reduced to −30 °C, the voltage of multiple cells in the middle of the stack fluctuate more drastic, and start-up takes 113 s. The performance and consistency of the stack did not decay after 20 cold start-up experiments, which indicates that our control strategies effectively avoided irreversible damage to the stack caused by freeze-thaw process.     

Chaos-Based transmitted-reference ultra-wideband communications

Transmitted-reference (TR) ultra-wideband (UWB) communication systems have gained increasing popularity for the usage in the low data rate application, due to its non-coherent receiver structure. In conventional TR system, non-coherency at the receiver is achieved by sending reference pulses prior to the data-bearing pulses. Then, at the receiver side, reference pulses are used as template signals for correlation with data-bearing pulses. Therefore, the orthogonality between reference and data pulses is obtained in time division multiple access (TDMA) fashion. However, the implementation of a wideband delay line is very difficult in the current low power integrated circuits. In this paper, a TR method called Chaos-Based TR (CB-TR) is proposed. In the proposed method, chaotic sequences are used to separate the reference and data pulses. Such approach exploits the benefits of chaotic signals, such as non-periodicity, easy-to-generate, impulse-like autocorrelation value and low cross-correlation value. Furthermore, in order to decrease the influence of some negative properties of conventional chaotic maps, a modified chaotic generator (MCS) is proposed. Simulation results over the IEEE 802.15.4a channel model show comparable bit error rate performance to other TR methods.