Equation-oriented Optimization of Cryogenic Systems for Coal Oxycombustion Power Generation

AbstractEfficient separation systems are essential to the development of economical CO2 capture system for fossil flue power plants. Air Separation Units (ASU) and CO2 Processing Units (CPU) are considering the best commercially available technologies for the O2/N2 and CO2/N2, O2, Ar separations in coal oxycombustion processes. Both of these systems operate at cryogenic temperatures and include self-integrated refrigeration cycles, making their design challenging. Several researchers have applied sensitivity tools available in the commercial flow sheet simulators to study and improve ASU and CPU systems for oxy-fired coal power plants. These studies are limited, however, as they neglect important interactions between design variables.In this paper, we apply an advanced equation-based flowsheet optimization framework to design these cryogenic separations systems. The key advantage of this approach is the ability to use state-of-the-art nonlinear optimization solvers that are capable of considering 100,000+ variables and constraints. This allows for multi-variable optimization of these cryogenic separations systems and their accompanying multi-stream heat exchangers. The effectiveness of this approach is demonstrated in two case studies. The optimized ASU designs requires 0.196 kWh/kg of O2, which are similar to a “low energy” design from American Air Liquide and outperforms other academic studies. Similarly, the optimized CPU requires 18% less specific separation energy than an academic reference case. Pareto (sensitivity) curves for the ASU and CPU systems are also presented. Finally, plans to apply the framework to simultaneously optimize an entire oxycombustion process are discussed

TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing

Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of a patient's symptoms hidden in the clinical record written in free text. Prior studies have shown that this system can be informationized and intelligentized with the aid of artificial intelligence (AI) technology, such as natural language processing (NLP). However, existing datasets are not of sufficient quality nor quantity to support the further development of data-driven AI technology in TCM. Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system -- syndrome differentiation (SD) -- and we introduce the first public large-scale dataset for SD, called TCM-SD. Our dataset contains 54,152 real-world clinical records covering 148 syndromes. Furthermore, we collect a large-scale unlabelled textual corpus in the field of TCM and propose a domain-specific pre-trained language model, called ZY-BERT. We conducted experiments using deep neural networks to establish a strong performance baseline, reveal various challenges in SD, and prove the potential of domain-specific pre-trained language model. Our study and analysis reveal opportunities for incorporating computer science and linguistics knowledge to explore the empirical validity of TCM theories.Comment: 10 main pages + 2 reference pages, to appear at CCL202

A Framework for Optimization and Quantification of Uncertainty and Sensitivity for Developing Carbon Capture Systems

AbstractUnder the auspices of the U.S. Department of Energy's Carbon Capture Simulation Initiative (CCSI), a Framework for Optimization and Quantification of Uncertainty and Sensitivity (FOQUS) has been developed. This tool enables carbon capture systems to be rapidly synthesized and rigorously optimized, in an environment that accounts for and propagates uncertainties in parameters and models. FOQUS currently enables (1) the development of surrogate algebraic models utilizing the ALAMO algorithm, which can be used for superstructure optimization to identify optimal process configurations, (2) simulation-based optimization utilizing derivative free optimization (DFO) algorithms with detailed black-box process models, and (3) rigorous uncertainty quantification through PSUADE. FOQUS utilizes another CCSI technology, the Turbine Science Gateway, to manage the thousands of simulated runs necessary for optimization and UQ. This computational framework has been demonstrated for the design and analysis of a solid sorbent based carbon capture system

Prediction of hydrocarbon source rock distribution using logging curves: A case study of Es32 source rock in Nanpu Sag, Huanghua depression, Bohai Bay Basin

The Es3 is the main hydrocarbon source rock system in the Nanpu Sag. Finally, the TOC and hydrocarbon potential of each sub depression of Es3were predicted. The study shows that the hydrocarbon source rocks of Es32 and Es34 sections are mainly of type II2 and type II1 respectively, with good organic matter type and high maturity. Biomarker compound parameters indicate that the Es32 section hydrocarbon source rocks developed in a semi-saline, low to medium terrestrial source organic matter supplied reduction environment with a high algal contribution; the Es34 section hydrocarbon source rocks formed in a freshwater, low terrestrial source supplied reduction environment with a medium-high algal contribution. The multiple linear regression method is more effective than the ΔlgR method in predicting hydrocarbon source rocks in the Nanpu Sag, and the prediction accuracy is higher; the correlation between TOC and S1 + S2 is the best in the model for predicting hydrocarbon potential. The TOC and hydrocarbon potential of the hydrocarbon source rocks in Es31 are generally low; the high value area of TOC and hydrocarbon potential of the hydrocarbon source rocks in Es32 is partly between the No. 1 tectonic zone and No. 5 tectonic zone in Linque sub depression, and the TOC and hydrocarbon potential of the hydrocarbon source rocks in Liunan sub depression are larger; the high value area of TOC and hydrocarbon potential of the hydrocarbon source rocks in Es34 is mainly concentrated in Shichang sub depression

Adoption of cloud computing as innovation in the organization

Over the years, there has been a heavy reliance on cloud computing as IT has innovated through time. In recent times cloud computing has grown monumentally. Many organizations rely on this technology to perform their business as usual and use it as a backbone of their companies' IT infrastructure. This paper investigates the organizational adaptation for cloud computing technology - reviewing case studies from various institutions and companies worldwide to provide a detailed analysis of innovative techniques with cloud computing. We investigate the features and delivery approaches cloud computing offers and the potential challenges and constraints we face when adopting cloud computing into the business setting. We also explore the cybersecurity elements associated with cloud computing, focusing on intrusion detection and prevention and understanding how that can be applied in the cloud. Finally, we investigate the future research directions for cloud computing and expand this paper into further articles with experiments and results

Customer Behavioral Trends in Online Grocery Shopping During COVID-19

The evolution of online shopping started when big players like Amazon began selling all types of merchandise. Customers understood the ease of shopping online, so the trend grew even stronger. It is therefore essential to conduct a study of online shopping usage and the perception of customers during COVID-19, especially in the grocery sector. In this study, approximately 28 respondents from 50 specifically targeted groups were surveyed, and data collection was undertaken through a structured questionnaire. The regression method was conducted to analyze the collected data. Additionally, 5 interviews were conducted to validate and support the findings. Customers definitely preferred online grocery shopping (OGS) services during COVID-19 due to safety, convenience, and government restrictions. The influential factors were very important in this case, like delivery times, good discounts, and the quality of products. Secondly, OGS services were more stable and alert during the pandemic situation, following the government’s rules and restrictions. Customers were extremely satisfied with the safety precautions during COVID-19, the assistance provided through helplines for support, and the increased customer reach to make groceries as accessible as other reputable online departments

Computationally Efficient Overmodulation Methods for Synchronous Motor Drive Systems

This paper presents two computationally efficient methods for selecting the optimal modulated voltage that can achieve superior dynamic performance for surface-mounted permanent magnet synchronous motors (SPMSMs). Specifically, when an SPMSM suffers a large reference or sudden load change, the controller might command a voltage reference which is beyond the range of voltages that a modulator can synthesize. In such cases, the transient behavior of the motor can deteriorate when the demanded voltage is not properly limited to the voltage boundary. To address this issue, a simple overmodulation method based on common-mode-saturation injection (CMSI) is proposed. This strategy comes with very low computational cost and can easily find the voltage vector on the boundary which is nearest to the reference voltage vector. Moreover, an alternative control method, referred to as quadratic program (QP) based deadbeat (DB) control, is proposed that also ensures optimal system performance during overmodualtion. According to this strategy, the control problem is formulated as a constrained QP, which is solved with an efficient solver based on an active-set method. Finally, extensive simulative and experimental investigations for an SPMSM are presented to demonstrate the effectiveness of the proposed overmodulation methods.acceptedVersionPeer reviewe

Printed Sowing of High-Density Mechanical Transplanted Hybrid Rice Can Reduce the Amount of Fertilizer Needed

In this study, we investigated how printed sowing machine transplanting impacts the yield of single-season rice by increasing the planting density and decreasing the amount of fertilizer needed. The study was aimed at exploring the relationships between the amount of fertilizer, transplanting density, and rice yield. During the rice growing season from 2019 to 2020 in the middle and lower reaches of the Yangtze River, six different field trials were conducted: low density and high fertilizer (LDHF), low density and low fertilizer (LDLF), middle density and high fertilizer (MDHF), middle density and low fertilizer (MDLF), high density and high fertilizer (HDHF), and high density and low fertilizer (HDLF). It turns out that compared to the LDHF, the thousand seed weight, the spikelets per panicle, the seed-setting rate, and the SPAD value at the filling stage decreased by 0.17% and 0.60%, 5.36% and 10.59%, 5.70% and 4.66%, and 17.52% and 4.93% in 2019 and 2020, respectively. However, compared to the LDHF, the panicles increased by 15.31% and 17.18%, respectively, the LAI at the filling stage increased by 1.92% and 0.48%, respectively, and the accumulation of dry matter above ground at the maturity stage also increased by 3.74% and 16.79% in 2019 and 2020, respectively. Therefore, compared to the yield of rice in the LDHF, the yield of rice in the HDLF increased by 5.06% and 6.64%. The yields of rice in the LDLF, MDHF, MDLF, and HDHF were lower than that in the LDHF and HDLF. The partial least squares path model (PLSPM) analysis showed that the fertilizer, density, and aboveground dry matter had positive effects on the yield, while the SPAD value and LAI had negative effects on the yield. This research shows that increasing the transplanting density can compensate for the yield loss caused by reducing the fertilizer amount. However, no combination of the transplanting density and fertilization amount can achieve the purpose of increasing the yield