Designing new molecules possessing desired properties is an important and difficult problem in the chemical, material, and pharmaceutical industries. The standard approach to this problem consists of an iterative form...
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Designing new molecules possessing desired properties is an important and difficult problem in the chemical, material, and pharmaceutical industries. The standard approach to this problem consists of an iterative formulation, synthesis, and evaluation cycle that is long, time-consuming, and expensive. Current computer-aided design approaches include heuristic and exhaustive searches, mathematical programming, and knowledge-based systems methods. While all these methods have a certain degree of appeal, they suffer from drawbacks in handling combinatorially large, nonlinear search spaces. Recently, a genetic algorithm-based approach was shown to be quite promising in handling these difficulties. In this paper, we investigate the performance of the basic genetic design framework for larger search spaces. We also present an extension to the basic genetic design framework by incorporating higher-level chemical knowledge to handle constraints such as chemical feasibility, stability, and complexity better. These advances are demonstrated with the aid of a polymer design case study.
Hydrogel scaffolds have numerous potential applications in the tissue engineering ***,tough hydrogel scaffolds implanted in vivo are seldom reported because it is difficult to balance biocompatibility and high mechani...
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Hydrogel scaffolds have numerous potential applications in the tissue engineering ***,tough hydrogel scaffolds implanted in vivo are seldom reported because it is difficult to balance biocompatibility and high mechanical *** by Chinese ramen,we propose a universal fabricating method(printing-P,training-T,cross-linking-C,PTC&PCT)for tough hydrogel scaffolds to fill this ***,3D printing fabricates a hydrogel scaffold with desired structures(P).Then,the scaffold could have extraordinarily high mechanical properties and functional surface structure by cycle mechanical training with salting-out assistance(T).Finally,the training results are fixed by photo-cross-linking processing(C).The tough gelatin hydrogel scaffolds exhibit excellent tensile strength of 6.66 MPa(622-fold untreated)and have excellent ***,this scaffold possesses functional surface structures from nanometer to micron to millimeter,which can efficiently induce directional cell ***,this strategy can produce bionic human tissue with mechanical properties of 10 kPa-10 MPa by changing the type of salt,and many hydrogels,such as gelatin and silk,could be improved with PTC or PCT *** experiments show that this scaffold can effectively promote the new generation of muscle fibers,blood vessels,and nerves within 4 weeks,prompting the rapid regeneration of large-volume muscle loss injuries.
The microbubble and microinterface play key roles in the development and progress of the technology in the field of chemical engineering,which has attracted broad attention from the scientific and industrial ***,Zhang...
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The microbubble and microinterface play key roles in the development and progress of the technology in the field of chemical engineering,which has attracted broad attention from the scientific and industrial ***,Zhang et *** a book about microinterfacial mass transfer intensification technology,where they systematically introduced scientific essence,reaction mechanism,equipment structure,and influence law of multiphase reaction process strengthened by microinterface.I believe this book can promote the technological innovation of microbubble-related processes,and also the development of the green chemical industry!
A formulation is presented for the optimal selection of both the equipment units and the network of connections for multipurpose batch plants so as to satisfy given production requirements for a variety of products. T...
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A formulation is presented for the optimal selection of both the equipment units and the network of connections for multipurpose batch plants so as to satisfy given production requirements for a variety of products. The model includes very general constraints and objective functions and permits to optimize simultaneously the structural aspects of the plant and the associated production schedule, accounting for both capital costs of equipment units and pipework, operating costs and revenues. A special novelty of the formulation is that it provides the possibility of generating directly the optimal connectivity between the process units, optimal sizing and costs of the transfer lines, and the optimal storage policies (and associated vessel sizes, if any) for stable intermediates without the need for a priori assumptions (e.g. unlimited storage, etc.). The resulting MILP problem is solved using a branch and bound method. Several examples are detailed, demonstrating the generality of the approach and the importance of including structural and operational aspects at the design stage. The flexibility of the formulation permits utilizing various design strategies, from a simultaneous optimization of all problem decisions (equipment, connections and schedule) to a sequential optimization of main equipment, followed by the design of connections etc. These strategies are also illustrated by an example.
Recent advances in artificial intelligence have changed the fundamental assumptions upon which the progress of computer-aided processengineering (modeling and methodologies) during the last 30 yr has been founded. Th...
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Recent advances in artificial intelligence have changed the fundamental assumptions upon which the progress of computer-aided processengineering (modeling and methodologies) during the last 30 yr has been founded. Thus, in certain instances, numerical computations today constitute inferior alternatives to qualitative and/or semi-quantitative models and procedures which can capture and utilize more broadly-based sources of knowledge. In this paper it will be shown how process development and design, as well as planning, scheduling, monitoring, analysis and control of process operations can benefit from improved knowledge-representation schemes and advanced reasoning control strategies. It will also be argued that the central challenge coming from research advances in artificial intelligence is "modeling the knowledge", i.e. modeling: (a) physical phenomena and the systems in which they occur;(b) information handling and processing systems;and (c) problem-solving strategies in design, operations and control. Thus, different strategies require different forms of declarative knowledge, and the success or failure of various design, planning, diagnostic and controlsystems depends on the extent of actively utilizable knowledge. Furthermore, this paper will outline the theoretical scope of important contributions from AI and what their impact has been and will be on the formulation and solution of processengineering problems.
This paper deals with self-localization of a mobile robot on the condition that no a-priori knowledge about the environment is available. The applied method features to be accurate, robust, independent of any artifici...
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This paper deals with self-localization of a mobile robot on the condition that no a-priori knowledge about the environment is available. The applied method features to be accurate, robust, independent of any artificial landmarks and feasible with such a moderate computational effort that all necessary tasks can be executed in real-time on a standard PC. The perception system used is a panorama laser range finder (PLRF) which takes scans of its present environment. A modified Dynamic Programming (DP) algorithm provides pattern matching and pattern recognition on the preprocessed panorama scans and thereby renders a qualitative fusion of the sensor data. For an exact quantitative estimate of the robot's current position, a robust localization module is employed. The knowledge gained about the environment along that way is stored in a self-growing, graph based map which combines geometrical information and topological restrictions. Preliminary experiments in a common office environment proved the reliability and efficiency of the system.
The scale up of hydrogen production to large scale production systems demands a structured approach to control these systems. With suitable control principles, optimization for operation can be achieved whilst maintai...
The scale up of hydrogen production to large scale production systems demands a structured approach to control these systems. With suitable control principles, optimization for operation can be achieved whilst maintaining flexibility. In this paper, existing control strategies are analyzed and compared for large scale electrolysis systems. It is argued, how modularization can enable the optimal distribution of set-points and enables the systems for capacity extension. An initial implementation in MATLAB is presented, showing the feasibility of the proposed power distribution concept.
This paper outlines the structure and implementational features of the DESIGN-KIT, a software support environment developed to aid processengineering activities such as: synthesis of process flowsheets, configuration...
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This paper outlines the structure and implementational features of the DESIGN-KIT, a software support environment developed to aid processengineering activities such as: synthesis of process flowsheets, configuration of control loops for complete plants, planning and scheduling of plant-wide operations and operational analysis. Based on object-oriented and data-driven programming styles, the paper discusses how the DESIGN-KIT is constructed to provide a rich repertory of declarative and procedural knowledge for the development of analytic- or design-oriented knowledge-based expert systems. A series of illustrations describe the construction of knowledge bases, graphic interface support, equation-oriented simulation and design, order-of-magnitude analysis, reasoning strategies and other facilities of the DESIGN-KIT.
Artificial neural networks based on a feedforward architecture and trained by the backpropagation technique have recently been applied to static fault diagnosis problems. The networks are used to classify measurement ...
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Artificial neural networks based on a feedforward architecture and trained by the backpropagation technique have recently been applied to static fault diagnosis problems. The networks are used to classify measurement vectors into a set of predefined categories that represent the various functional and malfunctional states of the process. While the networks can usually produce decision surfaces that correctly classify the training examples, regions of the input space not occupied by training data are classified arbitrarily. As a result, the networks may not accurately extrapolate from the training data. Although extrapolation is not required under ideal circumstances, in practice the network may be required to extrapolate when undersized training sets are used, when parent distributions of fault classes undergo shifts subsequent to training, and when the input data is corrupted by missing or biased sensors. These situations cause relatively high error rates for the neural classifier. A related probem is that the networks cannot detect when they lack the data for a reliable classification, a serious deficiency in many practical applications. Classifiers based on distance metrics assign regions of the input space according to their proximity to the training data, and thus extrapolation is not arbitrary but based on the most relevant data. Distance-based classifiers perform better under nonideal conditions and are to be preferred to neural network classifiers in diagnostic applications.
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