An investigation of the drivers,barriers, and incentives for environmental management systems in the Malaysian food and beverage industry

Clean Technologies and Environmental Policy - Food production and consumption is one of the major causes of global environmental degradation. One way to address environmental impacts in the food...     

An overview of utilizing water-in-diesel emulsion fuel in diesel engine and its potential research study

The need for more efficient energy usage and a less polluted environment are the prominent research areas that are currently being investigated by many researchers worldwide. Water-in-diesel emulsion fuel (W/D) is a promising alternative fuel that could fulfills such requests in that it can improve the combustion efficiency of a diesel engine and reduce harmful exhaust emission, especially nitrogen oxides (NO_x) and particulate matter (PM). To date, there have been many W/D emulsion fuel studies, especially regarding performance, emissions and micro-explosion phenomena. This review paper gathers and discusses the recent advances in emulsion fuel studies in respect of the impact of W/D emulsion fuel on the performance and emission of diesel engines, micro-explosion phenomena especially the factors that affecting the onset and strength of micro-explosion process, and proposed potential research area in W/D emulsion fuel study. There is an inconsistency in the results reported from previous studies especially for the thermal efficiency, brake power, torque and specific fuel consumption. However, it is agreed by most of the studies that W/D does result in an improvement in these measurements when the total amount of diesel fuel in the emulsion is compared with that of the neat diesel fuel. NO_x and PM exhaust gas emissions are greatly reduced by using the W/D emulsion fuel. Unburnt hydrocarbon (UHC) and carbon monoxide (CO) exhaust emissions are found to be increased by using the W/D emulsion fuel. The inconsistency of the experimental result can be related to the effects of the onset and the strength of the micro-explosion process. The factors that affect these measurements consist of the size of the dispersed water particle, droplet size of the emulsion, water-content in the emulsion, ambient temperature, ambient pressure, type and percentage of surfactant, type of diesel engine and engine operating conditions. Durability testing and developing the fuel production device that requires no/less surfactant are the potential research area that can be explored in future.     

Characterization and productivity of cassava waste and its use as an energy source

This study sought to quantify and characterize cassava waste as fuel. The wastes from three cultivars were collected to study and were divided into three distinct parts of the cassava plant: seed stem, thick stalks, and thin stalks. Physical and chemical analyzes were carried out to determine the elemental composition of the waste: volatile matter; fixed carbon; ash; moisture; lignin; cellulose; hemicellulose; ash composition and higher heating value were determined. We conducted a thermogravimetric analysis in oxidizing and inert atmospheres to study the behavior of the waste as fuel. The root productivity obtained ranged from 7.7 to 13.0 t ha⁻¹ yr⁻¹ on a dry basis (db), and the ratio between waste and roots varied from 0.36 to 0.91. The physical and chemical properties of cassava waste are analogous to those of woody biomass regarding the elemental composition, the higher heating value, and thermogravimetric analysis. Ash content varied from 2.5% to 3.5%, reaching around 6.0% in samples unwashed. Approximately 60% of the ashes are alkali oxides, especially P₂O₅, K₂O, and CaO, which have low melting points. The alkali index calculated suggests that there is a strong tendency that the combustion process leads to ash fouling and the formation of ash deposits.     

The capability of hibonite elongated grains to influence physical,microstructural, and mechanical properties of zirconia toughened alumina–CeO2–MgO ceramics

This work investigated the capability of hibonite (CaAl₁₂O₁₉) phase on structure and microstructure of zirconia toughened alumina–CeO₂–MgO ceramics. Three different additives (CeO₂, MgO and CaCO₃) were introduced into zirconia toughened alumina ceramics prepared by solid state reaction. X-ray diffraction and FESEM analyses were employed to observe the role of secondary phases especially hibonite and its influence on the microstructural features and other properties. Among the secondary phases present, EDX analysis revealed that hibonite phase contributed to elongated grains. Vickers indentations hinted a strong difference in the efficiency of compositional adjustment among the composites. The excellent Vickers hardness and fracture toughness results obtained for 3 wt.% CaCO₃ additions showed the appearance of 5.9% hibonite with the value of 1485 HV and 7.10 MPa·√m, respectively.     

Hardware transactional memory architecture with adaptive version management for multi-processor FPGA platforms

Multiprocessor embedded systems integrates diverse dedicated processing units to handle high performance applications such as in multimedia and network processing. However, lock-based synchronization limits the efficiency of such heterogeneous concurrent systems. Hardware Transactional Memory (HTM) is a promising approach in creating an abstraction layer for multi-threaded programming. However, HTM performance is application-specific and determined by version and conflict management configurations. Most previous HTM implementations for embedded system in literature were built on fixed version management that result in significant performance loss when transaction behaviour changes. In this paper, we propose a HTM targeted for embedded applications which is able to adapt its version management based on application behaviour at runtime. It is prototyped and analysed on Altera Cyclone IV platform. Random requests at different contention levels and different transaction sizes are used to verify the performance of the proposed HTM. Based on our experiments, lazy version management is able to obtain up to 12.82% speed-up compared to eager version management at high contention level. Meanwhile, eager version management obtains up to 37.84% speed-up compared to lazy version management at low contention. The adaptive mechanism is able to switch configuration at runtime based on applications behaviour for maximum performance.     

Investigation of indoor thermal comfort under transient conditions

In industrialized countries about 90% of the time is spent indoors. In indoor, thermal comfort can be basically predicted by the environmental parameters such as temperature, humidity, air velocity and by the personal parameters as activity and clothing resistance. In this study, a mathematical model of thermal interaction between human body and environment was established and the effect of clothing and air velocity was examined under transient conditions. By the developed model, human body has been separated to 16 segments and possible local discomforts are taken into consideration. Using the model, changes in the sensible and latent heat losses, skin temperature and wettedness, thermal comfort indices were calculated. In a hot environment latent heat loss increases by means of sweating. Because of over wetted skin, comfort sense goes worse. Especially, at feet and pelvis skin wettedness reaches maximum level. Sensible and latent heat losses rise and the skin temperature and wettedness decrease with increasing air velocity.     

Optical tomography hardware development for solid gas measurement using mixed projection

The ability to implement fan beam projection in parallel view in an optical tomography setup is one of the novelties of this research. This design involves a sensor jig specifically designed for parallel applications that does not involve a collimator. Therefore, the fan beam projections can also be implemented in the same sensor jig without difficulty. This method is a very practical solution for overcoming the disadvantages of parallel beam projection. Although the fan beam has its own disadvantages, combining the fan beam approach with the parallel beam approach is expected to further enhance the optical tomography image quality. The image quality can be measured using the Peak Signal-to-Noise Ratio (PSNR) and the Normalized Mean-Square Error (NMSE) parameters. The combination of the two approaches also eliminates the unwanted noise that appears when using parallel beam projection alone.     

Phases and thermoelectric properties of Ge1−x(Pb0.9Yb0.1)xTe alloys

A series of Ge_1−x(Pb_0.9Yb_0.1)_xTe alloys with x = 0.05, 0.10, 0.15, 0.20 and 0.30 were prepared by a conventional melting and a spark plasma sintering (SPS) techniques. The phases and thermoelectric properties for the alloys were investigated. The alloys consist of the GeTe-based rhombohedral single phase for x = 0.05, while both GeTe-based rhombohedral and PbTe-based rock-salt phases due to spinodal decomposition for the higher Pb content (x ≥ 0.10). The amount of the PbTe-based phase increases with the Pb content x increasing. All samples show p-type conduction. As Pb content x increases, the thermal conductivity reduces obviously, while the Seebeck coefficient and the electrical resistivity increases slightly. The maximum ZT of 1.4 at 723 K was eventually achieved in the sample with x = 0.15 due to its rather low thermal conductivity, from 3.7 W m⁻¹K⁻¹ at room temperature to 1.4 W m⁻¹K⁻¹ at 723 K (3.7–1.4 W m⁻¹K⁻¹), relative high Seebeck coefficient (46.5–141 μV K⁻¹) and relative low electrical resistivity (3.0–7.36 μΩ m).     

Prediction model for methanation reaction conditions based on a state transition simulated annealing algorithm optimized extreme learning machine

Methanation is the core process of synthetic natural gas, the performance of the entire reaction system depends on precise values of the reaction condition parameters. Accurate predictions of the CO conversion rate of the methanation reaction can eliminate time-consuming and complex steps in experiments and speed up the discovery of the best reaction conditions. However, the methanation reaction is an uncertain, highly complex, and highly nonlinear process. Thus, this paper proposes a machine learning prediction model for the methanation reaction to facilitate the subsequent search for optimal reaction conditions. The reaction temperature, pressure, hydrogen–carbon ratio, water vapor content, CO₂ content, and space velocity were selected as the condition variables. The CO conversion rate was the optimization objective. An extreme learning machine (ELM) was selected as a prediction model. Because the input weights and bias matrices of the ELM are randomly generated, an ELM based on a state transition simulated annealing (STASA-ELM) algorithm is proposed. The STASA algorithm was used to optimize the ELM to improve the accuracy and stability of the model. Five additional sets of experimental data were designed for the experiment, and the error between the experimental and predicted values was small. Thus, the STASA-ELM algorithm can accurately predict the conversion of CO for different values of reaction conditions.