Article
Agricultural Engineering
Jun-Hui Yang, Dong-Qi Huang, Yin-Ce Geng, Yi-Rong Ling, Nian-Si Fan, Ren-Cun Jin
Summary: Shortage of anammox sludge limits the application of anammox-based processes. In this study, three reactors were established using different seed sludges. After 27 days, anammox process with the highest nitrogen removal rate of 1.17 kg N m-3 d-1 was achieved using anammox granules and activated sludge. The dominant anammox bacteria shifted from Candidatus Kuenenia to Candidatus Brocadia, and quorum sensing-based regulation contributed to the growth of anammox bacteria.
BIORESOURCE TECHNOLOGY
(2023)
Article
Environmental Sciences
Xinxin Xu, Tingting Du, Du Guo, Xinye Jiang, Ming Zeng, Nan Wu, Chang Wang, Zongpeng Zhang
Summary: Anammox, an advanced nitrogen removal process, is inhibited by oxytetracycline (OTC) when the concentration reaches 2 mg/L. Machine learning models accurately predict nitrogen removal rates, showing a negative correlation with OTC under both short-term and long-term stress. Water quality parameters and hydraulic retention time play vital roles in experiments.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Agricultural Engineering
Dong Li, Hao Chen, Xin Gao, Jie Zhang
Summary: This study proposes a new PN/A/PN/A process to address the challenge of nitrite-oxidizing bacteria in treating low-strength ammonia wastewater. The influence of nitrite concentration on ammonia removal efficiency and nitrite accumulation rate is analyzed using a model. The study provides optimal parameters for the PN/A/PN/A process and achieves advanced nitrogen removal by controlling the nitrite concentration below a certain threshold.
BIORESOURCE TECHNOLOGY
(2022)
Article
Agricultural Engineering
Xinxin Xu, Shuang Liu, Ming Zeng, Hongli Li, Tingting Du, Nan Wu, Juanjuan Sun, Linlin Hao
Summary: This study investigated the inhibitory effects of different antibiotics on the anammox-based system, determining critical inhibition concentrations and using various models to describe the inhibition and recovery periods. Qualitatively, it was found that TC and OTC had the strongest inhibitory effects, while abundance of nitrogen functional genes was negatively correlated with antibiotics.
BIORESOURCE TECHNOLOGY
(2022)
Article
Engineering, Industrial
Shaochen Wang, Wende Tian, Chuankun Li, Zhe Cui, Bin Liu
Summary: In this paper, a mechanism-based deep learning method is proposed for the soft sensing of tray efficiency in the distillation process. By analyzing extreme alarm values and applying data clustering, key trays are identified and a surrogate model for tray efficiency is constructed using a deep learning model.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Review
Environmental Sciences
Quan Zhang, Nian-Si Fan, Jin-Jin Fu, Bao-Cheng Huang, Ren-Cun Jin
Summary: A comprehensive understanding of quorum sensing (QS) in the anammox process is essential for optimizing performance and applications.
CRITICAL REVIEWS IN ENVIRONMENTAL SCIENCE AND TECHNOLOGY
(2021)
Review
Environmental Sciences
Zhi-Cheng Zhao, Guo-Jun Xie, Bing-Feng Liu, De-Feng Xing, Jie Ding, Hong-Jun Han, Nan-Qi Ren
Summary: Partial nitritation-anammox (PNA) is a promising and energy-efficient process for sustainable nitrogen removal, but its wide applications are limited by long start-up period and instability in long-term operation. Quorum sensing (QS) in PNA process has been increasingly investigated as a way to manipulate microbial metabolism and overcome process limitations.
SCIENCE OF THE TOTAL ENVIRONMENT
(2021)
Article
Engineering, Chemical
Xinxin Xu, Hongli Li, Mingzhu Guo, Ming Zeng, Wei Liu, Nan Wu, Jiaqi Liang, Jingguo Cao
Summary: Anaerobic ammonium oxidation (anammox) is a promising process for nitrogen removal, but it is sensitive to environmental factors and toxic substances. Heavy metals significantly inhibit the efficiency and activity of anammox systems. Previous studies have not systematically investigated the effects and mechanisms of different heavy metals on anammox-based systems. This study analyzed big data from previous publications and found that ionic heavy metals have a greater impact on anammox activity than their nanoparticulate forms. Logistic kinetic model and Gaussian model were used to fit and predict the inhibitory effects of heavy metals on anammox. The study also revealed the inhibitory effects of heavy metals on specific nitrogen removal processes. The findings highlight the importance of considering ionic heavy metals and copper in anammox-based processes under heavy metal stress.
SEPARATION AND PURIFICATION TECHNOLOGY
(2022)
Article
Engineering, Electrical & Electronic
Shiwei Gao, Sulong Qiu, Zhongyu Ma, Ran Tian, Yanxing Liu
Summary: The challenges of the process industry can be effectively addressed by using soft sensor technology, which deals with the complexity of industrial environment and a large number of process variables. This paper proposes a SVAE-WGAN based method for supplementing data in harsh industrial environments, aiming to enhance the accuracy of soft-sensing models. The proposed method, which combines stacked variational autoencoder (SVAE) and Wasserstein generative adversarial network (WGAN), is optimized using an industrial process dataset. Experimental results indicate that the proposed SVAE-WGAN generation network outperforms traditional VAE, GAN, and WGAN generation networks, particularly in terms of RMSE. Additionally, the prediction precision of soft sensors can be improved through the supplement of training samples.
IEEE SENSORS JOURNAL
(2022)
Article
Automation & Control Systems
Zihao Zheng, Mumtaz Ali, Mehdi Jamei, Yong Xiang, Masoud Karbasi, Zaher Mundher Yaseen, Aitazaz Ahsan Farooque
Summary: Reference evapotranspiration can cause significant discrepancies in soil moisture and runoff, leading to uncertainties in drought warning systems. An innovative approach, MVMD-SoFeFilterGRU, is proposed to forecast one-day daily ETo based on Multivariate Variational Mode Decomposition hybridized with Soft Feature Filter and Gated Recurrent Unit.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Article
Chemistry, Analytical
Angzhi Fan, Yu Huang, Fei Xu, Sthitie Bom
Summary: The focus of this research is the soft-sensing regression prediction in semiconductor manufacturing using sensor data. A regressor based on Long Short-term Memory network is proposed and trained with two distinct loss functions. A novel piece-wise evaluation metric is introduced for mathematical assessment of model accuracy. Experimental results demonstrate that the proposed model is capable of accurately and early predicting various types of inspections in complicated manufacturing processes.
Article
Environmental Sciences
Lisheng Wang, Wancong Gu, Yanchen Liu, Peng Liang, Xiaoyuan Zhang, Xia Huang
Summary: This paper summarizes the challenges and solutions of mainstream anammox-based process by reviewing the literature of the past decade. The slow growth rate of anammox bacteria and the need for enhancing bacteria retention are identified as the main challenges. Various methods to culture anammox bacteria and improve their activity are discussed. Other challenges include the elimination of nitrite oxidizing bacteria (NOB) and achieving the ideal ratio of NH4+ and NO2-. To overcome these challenges, composite control strategies based on low sludge retention time (SRT) and limited aeration are suggested. Interference from low temperature and influent components in actual wastewater treatment is addressed, as well as the use of coupling processes to reduce nitrate concentration in the effluent. The paper concludes with future prospects for the mainstream anammox-based process.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Engineering, Environmental
Wenyu Li, Qiong Zhang, Jianwei Li, Ruitao Gao, Chengkun Kao, Xiyao Li, Yongzhen Peng
Summary: Nitrogen removal from saline municipal wastewater has become an area of interest, leading to the development of the energy-efficient technique of anaerobic ammonium oxidation (anammox) for advanced nitrogen removal. This study investigated the long-term salt adaptation and evolution mechanisms of an anammox-based process under different salinities. The results showed that the anammox bacteria had strong adaptability to salinity, with contribution levels of over 85% even at high salinity levels. Metagenomic sequencing revealed that the production of multiple nitrite substrates by specific bacteria ensured a high anammox contribution. This study provides valuable insights for the improved application of anammox-based processes in saline wastewater treatment.
CHEMICAL ENGINEERING JOURNAL
(2023)
Article
Computer Science, Artificial Intelligence
Xiao-Lu Song, Yan-Lin He, Xing-Yuan Li, Qun-Xiong Zhu, Yuan Xu
Summary: Data-driven soft sensing modeling is crucial for predicting key variables in the process industry. This paper proposes a virtual sample generation method called DAWI-VSG, based on data augmentation and weighted interpolation, to expand the soft sensing dataset with high-quality samples. The method decomposes the original dataset, synthesizes the features into a matrix, and generates new samples that approximate the original ones. The generated samples are used to improve the predictive power of soft sensing through outlier detection and weighted interpolation.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Automation & Control Systems
Xuanying Zhang, Yuzhu Wang, Lianjing Wei, Jinrong Jiang, Pengfei Lin, Hailong Liu
Summary: El Nino/Southern Oscillation (ENSO) is a complex event that affects the equatorial Pacific Ocean, causing abnormal Sea Surface Temperature (SST) changes. Accurate long-term ENSO predictions are crucial for global climate extremes and ecosystem stability. We have developed innovative methods, including transfer learning with simulated data, a new encoder-decoder structure with spatiotemporal memory cells, and mixed-precision computing to improve the accuracy and efficiency of ENSO forecasting. Our model outperforms existing statistical models and demonstrates precise predictions for El Nino, La Nina, and neutral ENSO conditions.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2023)
Review
Green & Sustainable Science & Technology
Himadri Rajput, Rahil Changotra, Prachi Rajput, Sneha Gautam, Anjani R. K. Gollakota, Amarpreet Singh Arora
Summary: The novel coronavirus originated in China has now spread to approximately 213 countries globally, posing a health calamity and international emergency. The emergence of COVID-19 has caused global declines in commodity prices, particularly affecting the oil market due to travel restrictions. The ultimate impact of the pandemic is expected to have long-lasting implications.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2021)
Review
Green & Sustainable Science & Technology
Amarpreet Singh Arora, Himadri Rajput, Rahil Changotra
Summary: The outbreak of COVID-19 is a highly contagious disease that has challenged healthcare systems worldwide, but South Korea's preventive measures and response policies have been effective in overcoming the crisis with confidence.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2021)
Review
Green & Sustainable Science & Technology
Rahil Changotra, Himadri Rajput, Prachi Rajput, Sneha Gautam, Amarpreet Singh Arora
Summary: The study delves into the trend and impact of COVID-19 spread in India, including case growth, lockdown measures, recovery rates, and comparisons with other countries. Findings indicate high recovery rates and low fatality rates in India, along with proactive prevention and control measures.
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2021)
Article
Green & Sustainable Science & Technology
Amarpreet Singh Arora, Alam Nawaz, Muhammad Abdul Qyyum, Sherif Ismail, Muhammad Aslam, Ahmed Tawfik, Choa Mun Yun, Moonyong Lee
Summary: The anammox technology is highly regarded for its low sludge production and energy efficiency, but faces challenges in dealing with inhibiting factors in waste streams. Detailed control and mitigation of these factors are crucial for successful implementation of the technology. Developing and implementing control strategies are key to addressing inhibiting factors and ensuring optimal performance.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Correction
Green & Sustainable Science & Technology
Himadri Rajput, Rahil Changotra, Prachi Rajput, Sneha Gautam, Anjani R. K. Gollakota, Amarpreet Singh Arora
ENVIRONMENT DEVELOPMENT AND SUSTAINABILITY
(2021)
Article
Green & Sustainable Science & Technology
Ali Rehman, Muhammad Abdul Qyyum, Kinza Qadeer, Fatima Zakir, Xiufen He, Alam Nawaz, Moonyong Lee, Li Wang
Summary: The study presents a systematic approach to improve the energy efficiency of the single mixed refrigerant (SMR) process by identifying improvement potentials, operational optimization, and uncovering further enhancement potentials. Results show that significant energy savings and high efficiency can be achieved through optimization and process retrofitting, indicating the importance of improving the interconnection between process equipment for overall efficiency.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Energy & Fuels
Muhammad Abdul Qyyum, Faisal Ahmed, Alam Nawaz, Tianbiao He, Moonyong Lee
Summary: This study explores the advanced process configuration modification and optimization of a dual mixed refrigerant (DMR) process for natural gas liquefaction, achieving a significant reduction in energy consumption and improvement in thermodynamic performance through the unique teaching-learning self-study optimization (TLSO) approach. The proposed cryogenic turbine-retrofitted DMR process showed higher exergy efficiency, coefficient of performance, and figure of merit compared to the conventional DMR process, providing potential performance enhancement opportunities. This research contributes valuable insights for process engineers in addressing energy efficiency challenges in both onshore and offshore LNG plants.
Article
Green & Sustainable Science & Technology
Alam Nawaz, Amarpreet Singh Arora, Dahee Yun, Choa Mun Yun, Moonyong Lee
Summary: The study introduces an innovative scheduling model based on mixed-integer nonlinear programming to optimize essential processing time, resulting in energy savings, cost reduction, and greenhouse gas emissions reduction. The model contributes to the development of the ProActive scheduling system, providing optimal operating conditions for reducing wastewater processing costs in line with sustainable development goals.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Thermodynamics
Muhammad Abdul Qyyum, Amjad Riaz, Ahmad Naquash, Junaid Haider, Kinza Qadeer, Alam Nawaz, Hyunhee Lee, Moonyong Lee
Summary: In summary, a simple, energy-efficient, and cost-effective process for H-2 liquefaction is proposed in this study. By utilizing three refrigeration cycles with optimal mixed-refrigerant compositions and a two-stage ortho-to-para conversion, the proposed process consumes less energy compared to the base design and a published base case.
ENERGY CONVERSION AND MANAGEMENT
(2021)
Article
Environmental Sciences
Nishu Goyal, Alam Nawaz, Kuldeep Singh Chandel, Devraja Devnarayan, Lalit Gupta, Siddharth Singh, Mohd Shariq Khan, Moonyong Lee, Amit Kumar Sharma
Summary: This review paper discusses the importance of carbon nanotubes in water and wastewater treatment, highlighting their unique material properties and advantages. It also emphasizes the need for a careful assessment of benefits versus risks in large-scale water treatment and the importance of ensuring environmental and human health safety.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2022)
Article
Automation & Control Systems
Alam Nawaz, Amarpreet Singh Arora, Wahid Ali, Nikita Saxena, Mohd Shariq Khan, Choa Mun Yun, Moonyong Lee
Summary: This article develops an intelligent human-machine interface for online monitoring and optimal operation of eco-efficient anaerobic ammonium oxidation (ANAMMOX) technology. By integrating optimized functionality data driven by supervisory control, it enhances energy efficiency and achieves sustainable operation.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Computer Science, Interdisciplinary Applications
Ahtesham Bakht, Alam Nawaz, Moonyong Lee, Hyunsoo Lee
Summary: A new deep learning framework is proposed for wastewater ingredient analysis and control. The framework utilizes preprocessed data from a deep neural network module and processes past data of the target variable using a recurrent neural network. It enables time-series analytics and outperforms other methods in terms of root mean square error and correlation coefficient in comparison tests.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Environmental Sciences
Muhammad Azizol Azmi, Kasypi Mokhtar, Noor Apandi Osnin, Suzanna Razali Chan, Gadah Albasher, Atif Ali, Alam Nawaz, Olakunle Oloruntobi, Lai Fatt Chuah
Summary: Coastal ecosystems play a crucial role in mitigating the impact of climate change, but they are increasingly vulnerable due to human activities and maritime accidents. This study highlights the importance of providing safety instructions for passengers to improve safety behavior among ferry workers. These instructions should include climate-related information and expand the concept of safe behavior to address climate-related incidents.
ENVIRONMENTAL RESEARCH
(2023)