module design
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Membrane Distillation (MD) is a membrane-based, temperature-driven water reclamation process. While research emphasis has been largely on membrane design, upscaling of MD has prompted advancements in energy-efficient module design and configurations. Apart from the four conventional configurations, researchers have come up with novel MD membrane module designs and configurations to improve thermal efficiency. While membrane design has been the focus of many studies, development of appropriate system configurations for optimal energy efficiency for each application has received considerable attention, and is a critical aspect in advancing MD configurations. This review assesses advancements in modified and novel MD configurations design with emphasis on the effects of upscaling and pilot scale studies. Improved MD configurations discussed in this review are the material gap MD, conductive gap MD, permeate gap MD, vacuum-enhanced AGMD/DCMD, submerged MD, flashed-feed MD, dead-end MD, and vacuum-enhanced multi-effect MD. All of these modified MD configurations are designed either to reduce the heat loss by mitigating the temperature polarization or to improve the mass transfer and permeate flux. Vacuum-enhanced MD processes and MD process with non-contact feed solution show promise at the lab-scale and must be further investigated. Hollow fiber membrane-based pilot scale modules have not yet been sufficiently explored. In addition, comparison of various configurations is prevented by a lack of standardized testing conditions. We also reflect on recent pilot scale studies, ongoing hurdles in commercialization, and niche applications of the MD process.

According to the existing literature, the existing engineering ideological and political curriculum distance education system has the problems of long response time and few concurrent users, which cannot meet the development needs of the professional talents of electronic engineering. Therefore, the design and research of the long-distance education system of the ideological and political course of electronic engineering specialty dependent on big data is proposed. Based on the analysis of the demand of distance education system, the overall structure of the distance education system is designed. The design of the system business functions mainly includes login verification module design, instant communication module design, online classroom module design, video answer module design and video back-view module design, and design the server cluster load balancing strategy according to big data technology, so as to realize the operation of the remote education system of the ideological and political courses of electronic engineering. The design system is shorter than response time while compared with existing system. and the number of users is more. It is fully proved that the design system has better application performance.

Reports of a project “Silicon Photonics and Manufacturing” under the Thrust 2 “Subsystem Integration and Silicon Nanophotonics” of an NSF-funded Centre for Integrated Access Networks.

Traditional teaching methods are limited to time and place, and the performance of dance teaching resource management is poor. This paper designs a dance teaching resource management system based on cloud computing. The functional structure of the system includes the core cloud computing teaching and teaching management application. The data management module is used to store the processed data in the data file, and respond to the retrieval request of dance teaching content publishing module and the remote image resource positioning request of multi-media management module. Design user courseware on demand process, using the existing dance teaching resources to learn. The experimental results show that the designed system has good performance, high stability and compatibility, and strong data storage capacity of teaching resources.

Limited battery power is a major challenge for wireless sensor network (WSN) in internet of things (IoT) applications, especially in hard-to-reach places that require periodic battery replacement. The energy harvesting application is intended as an alternative to maintain network lifetime by utilizing environmental energy. The proposed method utilized piezoelectricity to convert vibration or pressure energy into electrical energy through a modular piezoelectric energy harvesting design used to supply energy to sensor nodes in WSN. The module design consisted of several piezoelectric elements, of which each had a different character in generating energy. A bridge diode was connected to each element to reduce the feedback effect of other elements when pressure was exerted. The energy produced by the piezoelectric is an impulse so that the capacitor was used to quickly store the energy. The proposed module produced 7.436 μJ for each step and 297.4 μJ of total energy with pressure of a 45 kg load 40 times with specific experiments installed under each step. The energy could supply WSN nodes in IoT application with a simple energy harvesting system. This paper presents a procedure for measuring the energy harvested from a commonly available piezoelectric buzzer. The specific configurations of the piezoelectric and the experiment setups will be explained. Therefore, the output energy characteristics will be understood. In the end, the potentially harvested energy can be estimated. Therefore, the configuration of IoT WSN could be planned.

The growing interest in intensified process units that improve efficiency by combining several phenomena into one unit, has led to a loss in degrees of freedom when addressing the control scheme of these units. Previous work demonstrated that a novel module-based design approach to membrane reactors could improve the operability index of membrane reactor systems. This approach sought to decouple the phenomena to regain some degrees of freedom for the control system. However, the computational time to determine such an optimal module design made this class of design problems intractable to solve in a reasonable amount of time. This work proposes a set of design heuristics for a new module-based design approach for membrane reactors. These heuristics are used in combination with a genetic algorithm formulation to produce a novel, two-staged algorithm for the design and control of membrane reactor systems. This algorithm is developed in Python and uses rigorous membrane reactor models built in AVEVA Process Simulation. The proposed algorithm solves the original non-polynomial (NP) complexity problem in polynomial time (P), while still being able to find the optimal designs discovered in previous work through exhaustive methods.