智能工程与过程系统的集成优化

系统科学和信息技术的迅速发展为工业过程的计算机集成系统提供了基础,而以模糊专家系统和神经网络为代表的人工智能技术为解决过程工程中许多技术难题开辟了道路．本文着重讨论过程系统集成优化的概念,并重点论述人工神经网络、模糊专家系统、进化算法等智能辅助的系统优化技术．     

Prostate Cancer Radiogenomics—From Imaging to Molecular Characterization

Radiomics and genomics represent two of the most promising fields of cancer research, designed to improve the risk stratification and disease management of patients with prostate cancer (PCa). Radiomics involves a conversion of imaging derivate quantitative features using manual or automated algorithms, enhancing existing data through mathematical analysis. This could increase the clinical value in PCa management. To extract features from imaging methods such as magnetic resonance imaging (MRI), the empiric nature of the analysis using machine learning and artificial intelligence could help make the best clinical decisions. Genomics information can be explained or decoded by radiomics. The development of methodologies can create more-efficient predictive models and can better characterize the molecular features of PCa. Additionally, the identification of new imaging biomarkers can overcome the known heterogeneity of PCa, by non-invasive radiological assessment of the whole specific organ. In the future, the validation of recent findings, in large, randomized cohorts of PCa patients, can establish the role of radiogenomics. Briefly, we aimed to review the current literature of highly quantitative and qualitative results from well-designed studies for the diagnoses, treatment, and follow-up of prostate cancer, based on radiomics, genomics and radiogenomics research.     

酶工程：从人工设计到人工智能

计算机在酶工程中的应用使得酶的序列空间探索度不断被扩大。随着不同分子力场参数的建立,涌现出诸多以计算分子能量为基础的算法,并被用于酶的催化活性、稳定性、底物特异性等的改造与筛选。伴随计算机硬件的提升与算法的优化,从头设计全新功能的人工酶取得成功并得以发展。近年来,人工智能在蛋白质结构预测上不断获得突破,同时也被应用到酶的设计中。介绍了分子力场基础和酶设计与筛选的算法,重点阐述了从头设计的方法和成功案例,以及机器学习设计酶的流程和最新的研究进展,展望了人工智能在酶工程领域的未来发展,为酶的改造与全新功能的生物催化剂的设计助力。     

化工过程的智能混合建模方法及应用

随着人工智能技术和配套数据系统的快速发展,化工过程建模技术达到了新的高度,将多个机理模型和数据驱动模型以合理的结构加以组合的智能混合建模方法,可以综合利用化工过程的第一性原理及过程数据,结合人工智能算法以串联、并联或者混联的形式解决化工过程中的模拟、监测、优化和预测等问题,建模目的明确,过程灵活,形成的混合模型有着更好的整体性能,是近年来过程建模技术的重要发展趋势。本文围绕近年来针对化工过程的智能混合建模工作进行了总结,包括应用的机器学习算法、混合结构设计、结构选择等关键问题,重点论述了混合模型在不同任务场景下的应用。指出混合建模的关键在于问题和模型结构的匹配,而提高机理子模型性能,获取高质量宽范围的数据,深化对过程机理的理解,形成更有效率的混合建模范式,这些都是现阶段提高混合建模性能的研究方向。     

Calculating porosity and permeability from synthetic micro-CT scan images based on a hybrid artificial intelligence

Nowadays, lattice Boltzmann is one of the standard and exact methods of simulation in micro-CT images of rock. However, it has a high weakness in run time. Therefore, the effort in this article is to reach a comprehensive substitute method for permeability calculation with less run time than the lattice Boltzmann method. The other purposes are the automation of processing operations, preparation of images, and in the end, the calculation of porosity. The best way to achieve these outcomes is to use hybrid artificial intelligence. In this research work, comprehensive model architecture has been used to design a hybrid artificial intelligence to be able to calculate permeability and porosity in complex images. A thousand images were randomly generated with high complexity, which makes the model comprehensive and extensible, and image processing was applied. After that, the lattice Boltzmann method as the direct simulation was selected. Finally, the convolutional neural network and multilayer perceptron based on a new and comprehensive model were evaluated for the first time; the mean squared error resulting from the evaluation of training data is 0.01, and the test data is 0.03. Expert systems have been used as a subset of artificial intelligence for automated image processing and porosity calculation. In this way, problems related to the direct implementation of classical algorithms for image processing, models, and patterns related to machine learning and needing an expert were solved to an acceptable extent, and an error of less than 5% was achieved.     

Flooding Prevention in Distillation and Extraction Columns with Aid of Machine Learning Approaches

Flooding of separation columns is a severe limitation in the operation of distillation and liquid-liquid extraction columns. To observe operation conditions, machine learning algorithms are implemented to recognize the flooding behavior of separation columns on laboratory scale. Besides this, the investigated columns already provided the modular automation interface Module Type Package (MTP), which is used for data access of necessary sensor data. Hence, artificial intelligence (AI) tools with deep learning offer high potential for the process industry and allow to capture operating states that are otherwise difficult to detect or model. However, the advanced methods are only hesitantly applied in practice due to complex combination of operational sensing, data analysis, and active control of the equipment. This article provides an overview on how AI-based algorithms can be implemented in existing laboratory plants. Process sensor data as well as image data are used to model the flooding behavior of distillation and extraction columns for stable and robust operational conditions.     

生活垃圾智慧焚烧的研究现状及展望

对生活垃圾收运、存储、焚烧处置和污染物排放控制等过程的智能化监测、控制和管理等国内外研究现状进行了梳理,分析了物联网、焚烧状态诊断、污染物在线监测和人工智能算法等技术在生活垃圾焚烧处置过程中应用的优缺点,并对未来生活垃圾智慧焚烧技术的发展做出了展望,建议对现有技术进行系统化耦合,并通过大数据分析及云计算平台,构建智能化反馈和优化模型,进一步开发智慧焚烧技术与装备。     

Energy efficiency optimization strategies for greenhouse-based crop cultivation: A review

Worldwide, food scarcity is becoming a debatable concern among the scientific fraternity due to the increased populace, leading to decreased arable land. This has compelled us to explore various innovative and technological solutions, for example, large-scale greenhouse farming, to meet the surging demand for field production. In this context, research efforts have been continually made by various scientists and researchers to explore more control strategies/algorithms for keeping the indoor climate comfortable and enhancing the greenhouse's energy effectiveness. Considering this, an initiative was made to summarize the documented research findings in the last decade focusing on energy-efficient greenhouse-based crop cultivation. The findings of some studies considering selective parametric conditions have been presented in graphs/tables for reader clarity and discussion. Initially, the studies on existing energy efficient strategies, parameters, monitoring systems, sensing networks, and control algorithms have been discussed. A state of the art review found that control strategies are essential in low-energy greenhouses since they influence crop yield and cost. It was observed that advanced control algorithms and energy conservation in greenhouses received more attention due to wide spread application, high compatibility, low-cost, and user-friendly operations. In terms of future perspectives, it is anticipated that the development of machine learning, big data, and artificial intelligence, combining these technologies with traditional and advanced control strategies would lead to a revolution in the management of greenhouse energy.     

Knowledge expression,numerical modeling and optimization application of ethylene thermal cracking:From the perspective of intelligent manufacturing

Applications of process systems engineering (PSE) in plants and enterprises are boosting industrial reform from automation to digitization and intelligence.For ethylene thermal cracking,knowledge expression,numerical modeling and intelligent optimization are key steps for intelligent manufacturing.This paper provides an overview of progress and contributions to the PSE-aided production of thermal cracking;introduces the frameworks,methods and algorithms that have been proposed over the past 10 years and discusses the advantages,limitations and applications in industrial practice.An entire set of molecular-level modeling approaches from feedstocks to products,including feedstock molecular reconstruction,reaction-network auto-generation and cracking unit simulation are described.Multi-level control and optimization methods are exhibited,including at the operational,cycle,plant and enter-prise level.Relevant software packages are introduced.Finally,an outlook in terms of future directions is presented.     

Nephrotoxicity as a Complication of Chemotherapy and Immunotherapy in the Treatment of Colorectal Cancer,Melanoma and Non-Small Cell Lung Cancer

Acute kidney injury is a common complication of many medical procedures, including those used in cancer treatment. Both chemotherapy and immunotherapy may result in deterioration of kidney function, which may lead to an increase in mortality among patients with cancer. Antineoplastic agents can affect any element of the nephron, leading to the appearance of clinical symptoms such as proteinuria, hypertension, electrolyte disorders, glomerulonephritis, acute and chronic interstitial nephritis and acute kidney injury. The medical literature describing renal complications occurring during chemotherapeutic and immunotherapeutic treatment in neoplasms, such as colorectal cancer, non-small cell lung cancer and melanoma, was analysed. The immune system plays an important role in controlling the development of neoplasms and fighting them. Oncological treatment algorithms include immunotherapy as monotherapy, combined with chemotherapy or chemotherapy as monotherapy. In the treatment of the above-mentioned neoplasms immunotherapeutics are used, such as checkpoint inhibitors (CPI) (i.e., ipilimumab, pembrolizumab, nivolumab, atezolizumab), vascular endothelial growth factor (VEGF) inhibitors (i.e., bevacizumab, ramucirumab) and a variety of chemotherapeutic agents (irinotecan, capecitabine, oxaliplatin, gefitinib, erlotinib, gemcitabine, cisplatin, paclitaxel, carboplatin, doclitaxel, vinorelbine, topotecan, etoposide). In our article, we focused on the number and type of renal complications as well as on the time of their manifestation when using specific treatment regimens. Our analysis also includes case reports. We discussed treatment of immunological complications and adjustments of the dose of chemotherapeutic agents depending on the creatinine clearance. Analysing the data from the literature, when two immunotherapeutic agents are used together, the number of recorded renal complications increases. Bevacizumab and ramucirumab are the cause of the largest number of renal complications among the immunotherapeutic agents described above. Cisplatin is the best-described substance with the greatest nephrotoxic potential among the chemotherapeutic agents. Crucial for renal complications are also cancer stage, previous chemotherapy and other risk factors of AKI such as age, comorbidities and medications used. Due to the described complications during oncological treatment, including kidney damage, it seems necessary to elaborate standards of cooperation between oncologists and nephrologists both during and after treatment of a patient with cancer. Therefore, it is necessary to conduct further research and develop algorithms for management of a cancer patient, especially during such an intensive progress in oncology.     

Advances in De Novo Drug Design: From Conventional to Machine Learning Methods

De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including ma-chine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been em-ployed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and high-lights hot topics for further development.     

Computationally efficient methods for two multivariate fractionally integrated models

Abstract.  We discuss two distinct multivariate time-series models that extend the univariate ARFIMA (autoregressive fractionally integrated moving average) model. We discuss the different implications of the two models and describe an extension to fractional cointegration. We describe algorithms for computing the covariances of each model, for computing the quadratic form and approximating the determinant for maximum likelihood estimation and for simulating from each model. We compare the speed and accuracy of each algorithm with existing methods individually. Then, we measure the performance of the maximum likelihood estimator and of existing methods in a Monte Carlo. These algorithms are much more computationally efficient than the existing algorithms and are equally accurate, making it feasible to model multivariate long memory time series and to simulate from these models. We use maximum likelihood to fit models to data on goods and services inflation in the United States.     

Adaptive two-stage optimal designs for phase II clinical studies that allow early futility stopping

Abstract

Adaptive designs play an important role in contemporary clinical trials to make designs flexible and efficient. In cancer clinical trials, given a relatively small sample size, it is important to obtain as much information as possible during this phase. We propose a new adaptive optimal design that stops for futility only in the first stage as Simon’s two-stage design. The existing adaptive two-stage designs are often allowed to be stopped for futility or efficacy due to computational advantage. It is difficult to search for an optimal design with futility stopping only in the first stage by using efficient search algorithms; for example, the branch-and-bound algorithm. We have to use multiple computational techniques to search for the optimal design. The proposed adaptive design meets the important property of the monotonic property that the second stage sample size is a nonincreasing function of the number of responses from the first stage. In this article, we show that the proposed adaptive design always has a smaller expected sample size than Simon’s optimal design. We recommend it for use in practice as an alternative to the commonly used Simon’s design.     

Machine Learning Based Suggestions of Separation Units for Process Synthesis in Process Simulation

As part of Industry 4.0, workflows in the process industry are becoming increasingly digitalized. In this context, artificial intelligence (AI) methods are also finding their way into the process development. In this communication, machine learning (ML) algorithms are used to suggest suitable separation units based on simulated process streams. Simulations that have been performed earlier are used as training data and the information is learned by machine learning models implemented in Python. The trained models show good, reliable results and are connected to a process simulator using a .NET framework. For further optimization, a concept for the implementation of user feedback will be assigned. The results will provide the fundamental basis for future AI-based recommendation systems.     

An Overview of Predictors for Intrinsically Disordered Proteins over 2010–2014

The sequence-structure-function paradigm of proteins has been changed by the occurrence of intrinsically disordered proteins (IDPs). Benefiting from the structural disorder, IDPs are of particular importance in biological processes like regulation and signaling. IDPs are associated with human diseases, including cancer, cardiovascular disease, neurodegenerative diseases, amyloidoses, and several other maladies. IDPs attract a high level of interest and a substantial effort has been made to develop experimental and computational methods. So far, more than 70 prediction tools have been developed since 1997, within which 17 predictors were created in the last five years. Here, we presented an overview of IDPs predictors developed during 2010–2014. We analyzed the algorithms used for IDPs prediction by these tools and we also discussed the basic concept of various prediction methods for IDPs. The comparison of prediction performance among these tools is discussed as well.     

Process modeling and optimization of industrial ethylene oxide reactor by integrating support vector regression and genetic algorithm

This article presents an artificial intelligence‐based process modeling and optimization strategies, namely support vector regression–genetic algorithm (SVR‐GA) for modeling and optimization of catalytic industrial ethylene oxide (EO) reactor. In the SVR‐GA approach, an SVR model is constructed for correlating process data comprising values of operating and performance variables. Next, model inputs describing process operating variables are optimized using Genetic Algorithm (GAs) with a view to maximize the process performance. The GA possesses certain unique advantages over the commonly used gradient‐based deterministic optimization algorithms The SVR‐GA is a new strategy for chemical process modeling and optimization. The major advantage of the strategies is that modeling and optimization can be conducted exclusively from the historic process data wherein the detailed knowledge of process phenomenology (reaction mechanism, kinetics, etc.) is not required. Using SVR‐GA strategy, a number of sets of optimized operating conditions leading to maximized EO production and catalyst selectivity were obtained. The optimized solutions when verified in actual plant resulted in a significant improvement in the EO production rate and catalyst selectivity.     

仿生群体智能算法的演示教学

近年来,蚁群算法、蟑螂算法、微粒群算法、鱼群算法、蜂群算法等仿生算法层出不穷,由于这些群体智能算法具有良好的全局搜索能力,故在复杂的化工优化问题中得以广泛应用。因此,数值计算、优化方法等相关课程中应用演示教学法,将各种仿生算法的计算过程、算法参数对计算过程、计算性能的影响通过计算演示充分展示给学生看,可使枯燥、抽象的算法教学变得具体、形象、生动,显著提高学生的学习兴趣和教学质量。     

Comparative performance analysis of spectral estimation algorithms and computational optimization of a multispectral imaging system for print inspection

We have analyzed the performance of simulated multispectral systems for the spectral recovery of reflectance of printer inks from camera responses, including noise. To estimate reflectance we compared the performance of four algorithms which were not comparatively tested using the same data sets before. The criteria for selection of the algorithms were robustness against noise, amount of data needed as inputs (training set, spectral responsivities) and lacking of use of dimensionality reduction techniques. Three different sensor sets and training sets were used. We analyzed the differences in the spanning of the subspaces found for the three training sets, concluding that the ink reflectances have characteristic features. The best performance was obtained using the kernel and the radial basis function neural‐net‐based algorithms for the training set composed of printer inks reflectances, whereas for the other two training sets (composed of samples from the ColorChecker DC and Vhrel's reflectances' set) the quality of the recovered samples was more uniform among the algorithms. We also have performed an optimization to choose the best sensor set for the multispectral system with a reduced number of sensors. © 2012 Wiley Periodicals, Inc. Col Res Appl, 39, 16–27, 2014     

Modelling and optimisation of continuous catalytic regeneration process using bee colony algorithm

The continuous catalytic regeneration (CCR) reforming process optimisation leads to nonlinear programming with nonlinear quality constraints such as octane number and coke concentration on the catalytic particles. A typical CCR reforming process consists of four reactors with recycle. The reaction patterns and reactors have been mathematically modelled on a base of 12‐lumped kinetics reaction network derived from literature. The bee colony optimisation (BCO) algorithm is one of the most recent and efficient swarm intelligence‐based algorithms which simulates the foraging behaviour of honey bee colonies. The performance of the BCO algorithm in the process optimisation was compared with the genetic algorithm (GA). In the present work, BCO algorithm was used for optimising the CCR reforming process. The results show that the BCO algorithm reaches a better optimum point in a lower evaluation time and higher convergence rate with respect to the GA. © 2012 Canadian Society for Chemical Engineering