Abstract

Proton exchange membrane (PEM) fuel cell, a clean and alternative power source, has become a research hotspot in recent years. In general, PEM fuel cell-fed DC motor can only rotate unidirectionally. In this paper, a full-bridge converter is proposed to form PEM fuel cell-fed bidirectional DC motor system, and an adaptive backstepping sliding-mode control (ABSMC) is proposed to achieve the bidirectional angular velocity tracking and bus voltage regulation. The controloriented model of the PEM fuel cell-fed motor system is constructed. The control laws and adaptive laws are developed following the sliding-mode controller with backstepping approach to achieve bus voltage and angular velocity regulations. As a comparison, a traditional PI control is used, and the two controllers are compared through simulations. The results indicate that ABSMC show better control performance under the circumstance of system parameters variations and external disturbances no matter in bus voltage or angular velocity regulation. In particular, the PEM fuel cell can be protected via ABSMC since the controller reduced the impact of the load variation.

Abstract

Hydrate distribution controls the properties of hydrate-bearing sediments. Few methods cannot be available to understand hydrate distribution characteristics macroscopically at present. Designing a large-scaled pressure vessel possessing five measuring positions, we investigated hydrate distributions through the features of resistances in quartz sands and excess water. The results show that gas and hydrate governed the characteristics of resistances during hydrate formation stage. Different increasing fluctuations of resistances can show hydrate distributions. In our experiment, hydrates were considered to mainly distribute in the middle of horizontal artificial sediments. The resistances of hydrate-bearing sediments may offer a reference to hydrate distributions.

Abstract

Production and transport of petroleum fluids involves multiphase flow in pipelines. Handling of these fluids require knowledge on flow behavior whereby, experimental works is a key contributor. Many experimental works done were dedicated to horizontal and vertical pipelines while scarce works on inclined pipelines. The aim of this work was to present an experimental study on analysis of the effect of inclinations on gas-liquid flow patterns. In this study air and water was used in a 60 mm internal diameter pipe with 6 m length. The coverage is upward inclinations of 0 o to 90o . Flow patterns were recognized using high speed camera and conductance probes. Results shows that inclination has a significant effect on flow patterns distribution.

Abstract

With the ongoing digitalization of industrial production, an increasing number of energy measuring points are installed in manufacturing environments which enable promising use cases for machine learning applications. This paper presents a generic machine learning approach to forecast the very short term load of production machines which can be utilized as decision support basis for control schemes and measures to decrease energy costs. The presented approach is developed and evaluated on production machines of the ETA research factory at the Technische Universität Darmstadt. The results indicate that the developed approach is feasible and creates a precise very short term load forecasting model for different production machines.

Abstract

Liquefaction is recognized as one of the economical and feasible strategies for storing and transporting natural gas (NG). Among several NG liquefaction processes, the single mixed refrigerant (SMR) process are considered most suitable for offshore liquefied NG (LNG) production, mainly due to simple and compact design. However, these processes consume a tremendous amount of energy that leads to high operating costs. Therefore, process engineers and researchers associated with FLNG-FPSO industry are still trying to enhance the performance of the offshore LNG processes. Within this context, this study presents an innovative enhancement in SMR liquefaction process by introducing high-boiling mixed refrigerant self-recuperator. The proposed selfrecuperative SMR process is further enhanced by replacing Joule-Thomson (JT) valve with cryogenic liquid turbine, which ultimately reduces the overall process entropy generation. The proposed process gives LNG product with ≥94.0% liquefaction rate on the expense 4.98 kW/kmol-NG that is 19% and 25% lower than that of JT valve based self-recuperative SMR process and convention SMR process, respectively. The proposed enhancement can also be employed to other energy intensive refrigerant and liquefaction processes in order to improve an overall process efficiency.

Abstract

Platinum is of high importance for several emerging energy technologies which could help address our sustainability challenges such as climate change; but their increasing use could also lead to potential supply risk issues due to the over-concentration in mining and production. This paper used a dynamic material flow analysis (MFA) method to quantify current and future European platinum cycle by 2050 considering optimistic, pessimistic, and medium development of fuel cells, electrolysers, and other applications. We found when the primary platinum use shifted from autocatlysts to fuel cells, the European platinum demand will increase significantly and thus result in potential supply risks. Therefore, better end-of-life management systems to address the high losses of open-loop recycling (e.g., autocatalysts) and changing framework conditions (e.g., quantity and quality) of recyclability would be important. In addition, material efficiency and substitution will also become important in an increasingly constraint climate future.

Abstract

The rise of mixed-use buildings has contributed to the sustainable development of cities, but they still lack energy design guidelines. Energy consumption forecasting models have been crucial to the improvement of energy efficiency and sustainability of buildings, but their application to mixed-use buildings have been challenging and less tackled in literature. This study presented a novel forecasting model to predict energy consumption in mixed-use buildings using machine learning techniques. The model integrated kmeans clustering algorithm and support vector regression to improve predicting performance. The model was demonstrated to a case study considering mixed-use buildings in a tropical area. Clustering results found major differences in the consumption behavior of building clusters, especially on peaking characteristics. The proposed forecasting model was able to capture these variations due to clustering, leading to an increase in predicting performance. The model also performed within building modeling standards and better than statistical approaches in the literature.

Abstract

Polymer electrolyte membrane fuel cells have been considered as the potential solution for vehicle energy. Hydrogen is supplied to the anode of the fuel cell and electrochemically reacted with the oxygen of the cathode through the proton exchange membrane. The output current of the fuel cell varies under different vehicle operating conditions. Therefore, it is necessary to regulate the anode hydrogen excess ratio to maintain the high efficiency of the fuel cell. In this paper, a fuel cell anode hydrogen supply system is proposed based on a multiple-input multiple-output model predictive control (MPC) approach. The flow control valve and the hydrogen circulating pump via the proposed MPC are used to regulate the anode pressure and hydrogen excess ratio to meet the power demand of the vehicle. Comparing with the proportional-integral control result, the great control precision and transient response characteristics of the MPC can be achieved.

Abstract

The present paper describes an innovative and generalizable approach for applying fault mitigation strategies to fuel cell powered systems. Upon information on system State of Health (SoH) and Remaining Useful Life (RUL), the effects of faults occurring at stack or Balance of Plant (BoP) level can be mitigated via appropriate maneuvers. Model-based approach is proposed to derive useful performancerelated indicators per each system component. The model comprises two main parts: a nominal part, which provides the key variables behavior in nominal conditions, and a faulty part that can be used for fault identification purposes. The framework of the algorithm firstly addresses a monitoring phase, through which residuals are computed, and if one or more residuals overcome defined thresholds, a fault detection is triggered. Afterwards, fault isolation is performed by means of a Fault Signature Matrix (FSM) and the fault identification (i.e., its magnitude and time-behavior definition) is performed thanks to the faulty sub-models. Once the fault is characterized, several strategies (each designed according to four different fault magnitudes) are considered, and the most suitable one can be chosen and applied.

Abstract

The nanoneedle-decorated shell, nanoneedle, and nanosheet structures of cobalt phosphide (named CoPNDS, CoP-N, and CoP-S, respectively) are synthesized via one-step hydrothermal method and phosphilization process. The CoP-NDS is consisted of nanoneedle with about 400 nm of length and nanosheet with about 100 nm of thickness. The CoP-NDS hierarchical structure provides a larger specific surface area and allows more electrolyte to penetrate into the interior of the nanospace. The result shows that dye-sensitized solar cell (DSSC) using CoP-NDS as the counter electrode (CE) exhibits the best photoelectric conversion efficiency (η) of 8.80 ± 0.07%, as compared to CoP-N (8.50 ± 0.13%) and CoP-S (7.71 ± 0.12%) CEs under the illumination of 1 sun (AM1.5G, 100 mW cm-2 ). Furthermore, CoP-NDS CE shows higher photovoltaic performance than traditional Platinum (Pt) CE (8.24 ± 0.02%), demonstrating its potential to replace Pt as CE in DSSCs. To further explore the application, the photovoltaic performance of DSSCs under dim light condition is also studied. The results show that DSSCs with CoP-NDS CE achieve η’s of 14.22 ± 0.12%, 15.75 ± 0.02%, and 17.27 ± 0.14% at 1000, 3500, and 7000 lux, respectively. This study concludes that the low-cost and easy-to-fabricate CoP-NDS can be a promising candidate to replace the conventional Pt CE in DSSCs.