Clean hydrogen production with the Cu-Cl cycle - Progress of international consortium, I: Experimental unit operations

Advancement of the thermochemical copper-chlorine (Cu-Cl) cycle for hydrogen production is reviewed and discussed in this paper. Individual unit operations and their linkage into an integrated cycle are being developed by a Canadian consortium, as part of the Generation IV International Forum (GIF) for hydrogen production with the next generation of nuclear reactors. This paper focuses on the consortium’s latest advances on the Cu-Cl cycle, particularly with respect to hydrogen production with Canada’s Generation IV reactor, called SCWR (Super-Critical Water Reactor). Other heat sources may also be utilized for the Cu-Cl cycle, such as solar energy or industrial waste heat. In this first of two companion papers, recent developments in Canada’s nuclear hydrogen program are reported, specifically unit operation experiments of the Cu-Cl cycle and system integration. The following second companion paper will present system modeling with Aspen Plus, corrosion resistant materials, thermochemistry, safety, and reliability aspects of the Cu-Cl cycle.     

Clean hydrogen production with the Cu-Cl cycle - Progress of international consortium, II: Simulations, thermochemical data and materials

This second of two companion papers presents the latest advances of an international team on the thermochemical copper-chlorine (Cu-Cl) cycle of hydrogen production. It specifically focuses on simulations, thermochemical data, advanced materials, safety, reliability and economics of the Cu-Cl cycle. Aspen Plus simulations of various system configurations are performed to improve the cycle efficiency. In addition, simulations based on exergo-economic and exergy-cost-energy-mass (EXCEM) methods for system design are presented. Modeling of the linkage between nuclear and hydrogen plants demonstrates how the Cu-Cl cycle would be integrated with an SCWR (Super Critical Water Reactor; Canada’s Generation IV reactor). Chemical potentials, solubilities, formation of Cu(I) and Cu(II) complexes and properties of Cu₂OCl₂, Cu(I) and Cu(II) chloride species are reported. In addition, the development of new advanced materials with improved corrosion resistance is presented. In particular, the performance of new anode electrode structures and thermal spray coatings is presented. This companion set of two papers presents new advances in a range of key enabling technologies for the thermochemical copper-chlorine cycle.     

Critical processing temperature in the manufacture of fine‐celled plastic/wood‐fiber composite foams

The main benefits of incorporating wood fibers (WF) in plastics are the increased stiffness and lowered cost of the resulting composites. However, these improvements are usually accompanied by a reduction in the ductility and impact resistance. These shortcomings can be removed by effectively foaming and incorporating a fine‐celled structure in these composites. The volatiles released from WF during processing are known to deteriorate the cell structure. The maximum processing temperature, which affects the amount of volatiles released by the WF during extrusion of fine‐celled plastic/WF composite, affects the cell morphology. This study was undertaken to identify the critical temperature above which the cellular structure of WF composite foams is significantly deteriorated. To clearly identify the effects of the volatiles generated from WF on the cellular morphology, neither a chemical blowing agent nor a physical blowing agent was used in the foam processing. The experimental results show that regardless of the drying method, the highest processing temperature of plastic/WF composites should be minimized, preferably below 170°C, to avoid the adverse effects of the volatiles generated from the WF during processing. A method of estimating the emissions from WF during extrusion processing by using the TGA data is also proposed. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 621–629, 2004     

An iterative Q‐learning scheme for the global stabilization of discrete‐time linear systems subject to actuator saturation

In this paper, we propose a model‐free algorithm for global stabilization of linear systems subject to actuator saturation. The idea of gain‐scheduled low gain feedback is applied to develop control laws that avoid saturation and achieve global stabilization. To design these control laws, we employ the framework of parameterized algebraic Riccati equations (AREs). Reinforcement learning techniques are developed to find the solution of the parameterized ARE without requiring any knowledge of the system dynamics. In particular, we present an iterative Q‐learning scheme that searches for a low gain parameter and iteratively solves the parameterized ARE using the Bellman equation. Both state feedback and output feedback algorithms are developed. It is shown that the proposed scheme achieves model‐free global stabilization under bounded controls and convergence to the optimal solution of the ARE is achieved. Simulation results are presented that confirm the effectiveness of the proposed method.     

Advances in residual vector quantization: a review

Advances in residual vector quantization (RVQ) are surveyed. Definitions of joint encoder optimality and joint decoder optimality are discussed. Design techniques for RVQs with large numbers of stages and generally different encoder and decoder codebooks are elaborated and extended. Fixed-rate RVQs, and variable-rate RVQs that employ entropy coding are examined. Predictive and finite state RVQs designed and integrated into neural-network based source coding structures are revisited. Successive approximation RVQs that achieve embedded and refinable coding are reviewed. A new type of successive approximation RVQ that varies the instantaneous block rate by using different numbers of stages on different blocks is introduced and applied to image waveforms, and a scalar version of the new residual quantizer is applied to image subbands in an embedded wavelet transform coding system.     

Biodegradable and functionally superior starch–polyester nanocomposites from reactive extrusion

Biodegradable starch‐polyester polymer composites are useful in many applications ranging from numerous packaging end‐uses to tissue engineering. However the amount of starch that can form composites with polyesters without significant property deterioration is typically less than 25% because of thermodynamic immiscibility between the two polymers. We have developed a reactive extrusion process in which high amounts of starch (approx. 40 wt%) can be blended with a biodegradable polyester (polycaprolactone, PCL) resulting in tough nanocomposite blends with elongational properties approaching that of 100% PCL. We hypothesize that starch was oxidized and then crosslinked with PCL in the presence of an oxidizing/crosslinking agent and modified montmorillonite (MMT) organoclay, thus compatibilizing the two polymers. Starch, PCL, plasticizer, MMT organoclay, oxidizing/crosslinking agent and catalysts were extruded in a co‐rotating twin‐screw extruder and injection molded at 120° C. Elongational properties of reactively extruded starch‐PCL nanocomposite blends approached that of 100% PCL at 3 and 6 wt% organoclay. Strength and modulus remained the same as starch‐PCL composites prepared from simple physical mixing without any crosslinking. X‐ray diffraction results showed mainly intercalated flocculated behavior of clay at 1,3,6, and 9wt% organoclay. Scanning electron microscopy (SEM) showed that there was improved starch‐PCL interfacial adhesion in reactively extruded blends with crosslinking than in starch‐PCL composites without crosslinking. Dynamic mechanical analysis showed changes in primary α‐transition temperatures for both the starch and PCL fractions, reflecting crosslinking changes in the nanocomposite blends at different organoclay contents. Also starch‐polytetramethylene adipate‐co‐terephthalate (PAT) blends prepared by the above reactive extrusion process showed the same trend of elongational properties approaching that of 100% PAT. The reactive extrusion concept can be extended to other starch‐PCL like polymer blends with polymers like polyvinyl alcohol on one side and polybutylene succinate, polyhydroxy butyrate‐valerate and polylactic acid on the other to create cheap, novel and compatible biodegradable polymer blends with increased toughness. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 96: 1072–1082, 2005     

Optical flip-flops in a polarization-encoded optical shadow-casting scheme

We propose a novel scheme that optically implements various types of binary sequential logic elements. This is based on a polarization-encoded optical shadow-casting system. The proposed system architecture is capable of implementing synchronous as well as asynchronous sequential circuits owing to the inherent structural flexibility of optical shadow casting. By employing the proposed system, we present the design and implementation schemes of a J-K flip-flop and clocked R-S and D latches. The main feature of these flip-flops is that the propagation of the signal from the input plane to the output (i.e., processing) and from the output plane to the source plane (i.e., feedback) is all optical. Consequently the efficiency of these elements in terms of speed is increased. The only electronic part in the system is the detection of the outputs and the switching of the source plane.     

Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates

With the advent of technological advancements and the widespread Internet connectivity during the last couple of decades, social media platforms (such as Facebook, Twitter, and Instagram) have consumed a large proportion of time in our daily lives. People tend to stay alive on their social media with recent updates, as it has become the primary source of interaction within social circles. Although social media platforms offer several remarkable features but are simultaneously prone to various critical vulnerabilities. Recent studies have revealed a strong correlation between the usage of social media and associated mental health issues consequently leading to depression, anxiety, suicide commitment, and mental disorder, particularly in the young adults who have excessively spent time on social media which necessitates a thorough psychological analysis of all these platforms. This study aims to exploit machine learning techniques for the classification of psychotic issues based on Facebook status updates. In this paper, we start with depression detection in the first instance and then expand on analyzing six other psychotic issues (e.g., depression, anxiety, psychopathic deviate, hypochondria, unrealistic, and hypomania) commonly found in adults due to extreme use of social media networks. To classify the psychotic issues with the user's mental state, we have employed different Machine Learning (ML) classifiers i.e., Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes (NB), and K-Nearest Neighbor (KNN). The used ML models are trained and tested by using different combinations of features selection techniques. To observe the most suitable classifiers for psychotic issue classification, a cost-benefit function (sometimes termed as ‘Suitability’) has been used which combines the accuracy of the model with its execution time. The experimental evidence argues that RF outperforms its competitor classifiers with the unigram feature set.     

Continuous supercritical carbon dioxide processing of palm oil

Crude palm oil was processed by continuous supercritical carbon dioxide. The process reduces the contents of free fatty acids, monoglycerides and diglycerides, certain triglycerides, and some carotenes. The refined palm oil from the process has less than 0.1% free fatty acids, higher carotene content, and low diglycerides. Solubility of palm oil in supercritical carbon dioxide increased with pressure. A co-solvent improves the refining process of palm oil.