Chemical engineers rely on models for design, research, and daily decision-making, often with potentially large financial and safety implications. Previous efforts a few decades ago to combine artificial intelligence and chemical engineering for modeling were unable to fulfill the expectations. In the last five years, the increasing availability of data and computational resources has led to a resurgence in machine learning-based research. Many recent efforts have facilitated the roll-out of machine learning techniques in the research field by developing large databases, benchmarks, and representations for chemical applications and new machine learning frameworks. Machine learning has significant advantages over traditional modeling techniques, including flexibility, accuracy, and execution speed. These strengths also come with weaknesses, such as the lack of interpretability of these black-box models. The greatest opportunities involve using machine learning in time-limited applications such as real-time optimization and planning that require high accuracy and that can build on models with a self-learning ability to recognize patterns, learn from data, and become more intelligent over time. The greatest threat in artificial intelligence research today is inappropriate use because most chemical engineers have had limited training in computer science and data analysis. Nevertheless, machine learning will definitely become a trustworthy element in the modeling toolbox of chemical engineers.
With the concepts of Industry 4.0 and smart manufacturing gaining popularity, there is a growing notion that conventional manufacturing will witness a transition toward a new paradigm, targeting innovation, automation, better response to customer needs, and intelligent systems. Within this context, this review focuses on the concept of cyber-physical production system (CPPS) and presents a holistic perspective on the role of the CPPS in three key and essential drivers of this transformation: data-driven manufacturing, decentralized manufacturing, and integrated blockchains for data security. The paper aims to connect these three aspects of smart manufacturing and proposes that through the application of data-driven modeling, CPPS will aid in transforming manufacturing to become more intuitive and automated. In turn, automated manufacturing will pave the way for the decentralization of manufacturing. Layering blockchain technologies on top of CPPS will ensure the reliability and security of data sharing and integration across decentralized systems. Each of these claims is supported by relevant case studies recently published in the literature and from the industry; a brief on existing challenges and the way forward is also
provided.
Based on the analysis of the characteristics and operation status of the process industry, as well as the development of the global intelligent manufacturing industry, a new mode of intelligent manufacturing for the process industry, namely, deep integration of industrial artificial intelligence and the Industrial Internet with the process industry, is proposed. This paper analyzes the development status of the existing three-tier structure of the process industry, which consists of the enterprise resource planning, the manufacturing execution system, and the process control system, and examines the decision-making, control, and operation management adopted by process enterprises. Based on this analysis, it then describes the meaning of an intelligent manufacturing framework and presents a vision of an intelligent optimal decision-making system based on human–machine cooperation and an intelligent autonomous control system. Finally, this paper analyzes the scientific challenges and key technologies that are crucial for the successful deployment of intelligent manufacturing in the process industry.
The world's increasing population requires the process industry to produce food, fuels, chemicals, and consumer products in a more efficient and sustainable way. Functional process materials lie at the heart of this challenge. Traditionally, new advanced materials are found empirically or through trial-and-error approaches. As theoretical methods and associated tools are being continuously improved and computer power has reached a high level, it is now efficient and popular to use computational methods to guide material selection and design. Due to the strong interaction between material selection and the operation of the process in which the material is used, it is essential to perform material and process design simultaneously. Despite this significant connection, the solution of the integrated material and process design problem is not easy because multiple models at different scales are usually required. Hybrid modeling provides a promising option to tackle such complex design problems. In hybrid modeling, the material properties, which are computationally expensive to obtain, are described by data-driven models, while the well-known process-related principles are represented by mechanistic models. This article highlights the significance of hybrid modeling in multiscale material and process design. The generic design methodology is first introduced. Six important application areas are then selected: four from the chemical engineering field and two from the energy systems engineering domain. For each selected area, state-ofthe- art work using hybrid modeling for multiscale material and process design is discussed. Concluding remarks are provided at the end, and current limitations and future opportunities are pointed out.
Due to growing concerns regarding climate change and environmental protection, smart power generation has become essential for the economical and safe operation of both conventional thermal power plants and sustainable energy. Traditional first-principle model-based methods are becoming insufficient when faced with the ever-growing system scale and its various uncertainties. The burgeoning era of machine learning (ML) and data-driven control (DDC) techniques promises an improved alternative to these outdated methods. This paper reviews typical applications of ML and DDC at the level of monitoring, control, optimization, and fault detection of power generation systems, with a particular focus on uncovering how these methods can function in evaluating, counteracting, or withstanding the effects of the associated uncertainties. A holistic view is provided on the control techniques of smart power generation, from the regulation level to the planning level. The benefits of ML and DDC techniques are accordingly interpreted in terms of visibility, maneuverability, flexibility, profitability, and safety (abbreviated as the ″5-TYs”), respectively. Finally, an outlook on future research and applications is presented.
This paper synchronizes control theory with computer vision by formalizing object tracking as a sequential decision-making process. A reinforcement learning (RL) agent successfully tracks an interface between two liquids, which is often a critical variable to track in many chemical, petrochemical, metallurgical, and oil industries. This method utilizes less than 100 images for creating an environment, from which the agent generates its own data without the need for expert knowledge. Unlike supervised learning (SL) methods that rely on a huge number of parameters, this approach requires far fewer parameters, which naturally reduces its maintenance cost. Besides its frugal nature, the agent is robust to environmental uncertainties such as occlusion, intensity changes, and excessive noise. From a closed-loop control context, an interface location-based deviation is chosen as the optimization goal during training. The methodology showcases RL for real-time object-tracking applications in the oil sands industry. Along with a presentation of the interface tracking problem, this paper provides a detailed review of one of the most effective RL methodologies: actor–critic policy.
Data-driven process-monitoring methods have been the mainstream for complex industrial systems due to their universality and the reduced need for reaction mechanisms and first-principles knowledge. However, most data-driven process-monitoring methods assume that historical training data and online testing data follow the same distribution. In fact, due to the harsh environment of industrial systems, the
collected data from real industrial processes are always affected by many factors, such as the changeable operating environment, variation in the raw materials, and production indexes. These factors often cause the distributions of online monitoring data and historical training data to differ, which induces a model mismatch in the process-monitoring task. Thus, it is difficult to achieve accurate process monitoring when a model learned from training data is applied to actual online monitoring. In order to resolve the problem of the distribution divergence between historical training data and online testing data that is induced by changeable operation environments, a robust transfer dictionary learning (RTDL) algorithm is proposed in this paper for industrial process monitoring. The RTDL is a synergy of representative learning and domain adaptive transfer learning. The proposed method regards historical training data and online testing data as the source domain and the target domain, respectively, in the transfer learning problem. Maximum mean discrepancy regularization and linear discriminant analysis-like regularization are then incorporated into the dictionary learning framework, which can reduce the distribution divergence between the source domain and target domain. In this way, a robust dictionary can be learned even if the characteristics of the source domain and target domain are evidently different under the interference of a realistic and changeable operation environment. Such a dictionary can effectively improve the performance of process monitoring and mode classification. Extensive experiments including a numerical simulation and two industrial systems are conducted to verify the efficiency and superiority of the proposed method.
The shortage of computation methods and storage devices has largely limited the development of multiobjective optimization in industrial processes. To improve the operational levels of the process industries, we propose a multi-objective optimization framework based on cloud services and a cloud distribution system. Real-time data from manufacturing procedures are first temporarily stored in a local database, and then transferred to the relational database in the cloud. Next, a distribution system with elastic compute power is set up for the optimization framework. Finally, a multi-objective optimization model based on deep learning and an evolutionary algorithm is proposed to optimize several conflicting goals of the blast furnace ironmaking process. With the application of this optimization service in a cloud factory, iron production was found to increase by 83.91 t∙d-1, the coke ratio decreased 13.50 kg∙t-1, and the silicon content decreased by an average of 0.047%.
Cell voltage is a widely used signal that can be measured online from an industrial aluminum electrolysis cell. A variety of parameters for the analysis and control of industrial cells are calculated using the cell voltage. In this paper, the frequency segmentation of cell voltage is used as the basis for designing filters to obtain these parameters. Based on the qualitative analysis of the cell voltage, the sub-band instantaneous energy spectrum (SIEP) is first proposed, which is then used to quantitatively represent the characteristics of the designated frequency bands of the cell voltage under various cell conditions. Ultimately, a cell condition-sensitive frequency segmentation method is given. The proposed frequency segmentation method divides the effective frequency band into the [0, 0.001] Hz band of low-frequency signals and the [0.001, 0.050] Hz band of low-frequency noise, and subdivides the low-frequency noise into the [0.001, 0.010] Hz band of metal pad abnormal rolling and the [0.01, 0.05] Hz band of sub-lowfrequency noise. Compared with the instantaneous energy spectrum based on empirical mode decomposition, the SIEP more finely represents the law of energy change with time in any designated frequency band within the effective frequency band of the cell voltage. The proposed frequency segmentation method is more sensitive to cell condition changes and can obtain more elaborate details of online cell condition information, thus providing a more reliable and accurate online basis for cell condition monitoring and control decisions.
A herbal prescription in traditional Chinese medicine (TCM) has great complexity, with multiple components and multiple targets, making it extremely challenging to determine its bioactive compounds. Yinchenhao Tang (YCHT) has been extensively used for the treatment of jaundice disease. Although many studies have examined the efficacy and active ingredients of YCHT, there is still a lack of an in-depth systematic analysis of its effective components, mechanisms, and potential targets—especially one based on clinical patients. This study established an innovative strategy for discovering the potential targets and active compounds of YCHT based on an integrated clinical and animal experiment platform. The serum metabolic profiles and constituents of YCHT in vivo were determined by ultra-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UPLC-Q-ToF-MS)-based metabolomics combined with a serum pharmacochemistry method. Moreover, a compound–target–pathway network was constructed and analyzed by network pharmacology and ingenuity pathway analysis (IPA). We found that eight active components could modulate five key targets. These key targets were further verified by enzyme-linked immunosorbent assay (ELISA), which indicated that YCHT exerts therapeutic effects by targeting cholesterol 7a-hydroxylase (CYP7A1), multidrug-resistance-associated protein 2 (ABCC2), multidrug-resistance-associated protein 3 (ABCC3), uridine diphosphate glucuronosyl transferase 1A1 (UGT1A1), and farnesoid X receptor (FXR), and by regulating metabolic pathways including primary bile acid biosynthesis, porphyrin and chlorophyll metabolism, and biliary secretion. Eight main effective compounds were discovered and correlated with the key targets and pathways. In this way, we demonstrate that this integrated strategy can be successfully applied for the effective discovery of the active compounds and therapeutic targets of an herbal prescription.
Developing high-performing oxygen evolution reaction (OER) electrocatalysts under high-current operation conditions is critical for future commercial applications of alkaline water electrolysis for clean energy generation. Herein, we prepared a three-dimensional (3D) bimetallic oxyhydroxide hybrid grown on a Ni foam (NiFeOOH/NF) prepared by immersing Ni foam (NF) into Fe(NO3)3 solution. In this unique 3D structure, the NiFeOOH/NF hybrid was composed of crystalline Ni(OH)2 and amorphous FeOOH evenly grown on the NF surface. As a bimetallic oxyhydroxide electrocatalyst, the NiFeOOH/NF hybrid exhibited excellent catalytic activity, surpassing not only the other reported Ni–Fe based electrocatalysts, but also the commercial Ir/C catalyst. In situ electrochemical Raman spectroscopy demonstrated the active FeOOH and NiOOH phases involved in the OER process. Profiting from the synergy of Fe and Ni catalytic sites, the NiFeOOH/NF hybrid delivered an outstanding OER performance under challenging industrial conditions in a 10.0 mol∙L-1 KOH electrolyte at 80 ºC, requiring potentials as small as 1.47 and 1.51 V to achieve the super-high catalytic current densities of 100 and 500 mA∙cm-2, respectively.
The rapid advance of autonomous vehicles (AVs) has motivated new perspectives and potential challenges for existing modes of transportation. Currently, driving assistance systems of Level 3 and below have been widely produced, and several applications of Level 4 systems to specific situations have also been gradually developed. By improving the automation level and vehicle intelligence, these systems
can be further advanced towards fully autonomous driving. However, general development concepts for Level 5 AVs remain unclear, and the existing methods employed in the development processes of Levels 0–4 have been mainly based on task-driven function development related to specific scenarios. Therefore, it is difficult to identify the problems encountered by high-level AVs. The essential logical
and physical mechanisms of vehicles have hindered further progression towards Level 5 systems. By exploring the physical mechanisms behind high-level autonomous driving systems and analyzing the essence of driving, we put forward a coordinated and balanced framework based on the brain–cerebellum–organ concept through reasoning and deduction. Based on a mixed mode relying on the crow inference and parrot imitation approach, we explore the research paradigm of autonomous learning and prior knowledge to realize the characteristics of self-learning, self-adaptation, and self-transcendence for AVs. From a systematic, unified, and balanced point of view and based on least action principles and unified safety field concepts, we aim to provide a novel research concept and develop an effective approach for the research and development of high-level AVs, specifically at Level 5.
Wheat blast, caused by the fungus Magnaporthe oryzae Triticum (MoT) pathotype, is a devastating disease persistent in South America and Bangladesh. Since MoT generally fails to cause visual symptoms in wheat until the heading stage when the infection would have advanced, disease control by fungicide application solely based on the detection of visual symptoms is ineffective. To develop an accurate and sensitive method to detect MoT at the seedling and vegetative stages for disease control, we sequenced the genomes of two MoT isolates from Brazil and identified two DNA fragments, MoT-6098 and MoT-6099, that are present in the MoT genome but not in the genome of the rice-infecting M. oryzae Oryzae (MoO) pathotype. Using polymerase chain reaction (PCR), we confirmed the specificity of the two markers in 53 MoT and MoO isolates from South America and Bangladesh. To test the efficiency of the two markers, we first established a loop-mediated isothermal amplification (LAMP) method to detect MoT at isothermal conditions, without the use of a PCR machine. Following this, we used the Cas12a protein and guide RNAs (gRNAs) to target the MoT-6098 and MoT-6099 sequences. The activated Cas12a showed indiscriminate single-stranded DNase (ssDNAase) activity. We then combined target-dependent Cas12a ssDNase activation with recombinase polymerase amplification (RPA) and nucleic acid lateral flow immunoassay (NALFIA) to develop a method that accurately, sensitively, and cost-effectively detects MoT-specific DNA sequences in infected wheat plants. This novel technique can be easily adapted for the rapid detection of wheat blast and other important plant diseases in the field.
The relationship between environmental quality and economic growth has been a hot topic for decades. After years of rapid industrialization and urbanization, China's environmental challenges are approaching a turning point. Following the principles of ecological civilization construction, China is on its way to maintaining high-quality and green economic development. On 10 June 2020, the Chinese Government reported the key findings of the Second National General Survey of Pollution Sources (fiscal year 2017), which provides strong quantitative evidence of progress toward ecological civilization. In terms of our comparison between the two National General Surveys in 2007 and 2017, it was found that environmental pollution, measured in terms of many wastewater and air emission pollutant discharges, is decreasing despite the steady growth in economic activities—and at a noticeably fast pace. Other national and local governments can adopt some of China's ecological civilization practices, within their own individual contexts.