Trajectory optimization techniques in chemical reaction engineering: I. Boundary condition iteration methods

AIChE JournalVolume 18, Issue 4 p. 867-869 Communication to the Editor Trajectory optimization techniques in chemical reaction engineering. II. Comparison of the methods R. K. Jaspan, R. K. Jaspan Department of Chemical and Petroleum Engineering University of Pittsburgh, Pittsburgh, Pennsylvania 15213Search for more papers by this authorJ. Coull, Corresponding Author J. Coull Department of Chemical and Petroleum Engineering University of Pittsburgh, Pittsburgh, Pennsylvania 15213Department of Chemical and Petroleum Engineering University of Pittsburgh, Pittsburgh, Pennsylvania 15213Search for more papers by this author R. K. Jaspan, R. K. Jaspan Department of Chemical and Petroleum Engineering University of Pittsburgh, Pittsburgh, Pennsylvania 15213Search for more papers by this authorJ. Coull, Corresponding Author J. Coull Department of Chemical and Petroleum Engineering University of Pittsburgh, Pittsburgh, Pennsylvania 15213Department of Chemical and Petroleum Engineering University of Pittsburgh, Pittsburgh, Pennsylvania 15213Search for more papers by this author First published: July 1972 https://doi.org/10.1002/aic.690180440Citations: 7AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Citing Literature Volume18, Issue4July 1972Pages 867-869 RelatedInformation

Polymer Reaction Engineering in China

In order to decrease the fluid drag on an underwater robot manipulator, an optimal trajectory method based on the variational method is presented. By introducing the adjoint variables, which are Lagrange multipliers, we formulate a Lagrange function under certain constraints related to the target angle, target angular velocity, and dynamic equation of the robot manipulator. The state equation (the partial differentiation of the Lagrange function with respect to the state variables), adjoint equation (the partial differentiation of the Lagrange function with respect to the adjoint variables), and sensitivity equation (the partial differentiation of the Lagrange function with respect to torques) can be derived from the stationary conditions of the Lagrange function. Using the state equation, we can calculate the state variables (angles, angular velocities, and angular acceleration) at every time step in the forward time direction. These state variables are stored as data at every time step. Next, by using the adjoint equation, we can calculate the adjoint variables by using these state variables at every time step in the backward time direction. These adjoint variables are stored as data at every time step. Third, the sensitivity equation is calculated by using both the state variables and the adjoint variables. Finally, the optimal trajectory of the manipulator is obtained using the sensitivities. The proposed method is applied to the problem of two-link manipulators. It can obtain the optimal drag reduction trajectory of the manipulator under the constraints mentioned above.

Chemical reaction engineering—the discipline whose goal is to move chemical transformations from the laboratory to industrial production—encompasses issues that range from the molecular level to the design of huge production plants. The chemical engineers who design industrial processes need to consider the minute details of how reactions proceed, the grand overview of energy and materials flow into and out of plants, and everything in between. At the 14th International Symposium on Chemical Reaction Engineering (ISCRE-14) in Brugge, Belgium, last month, 500 attendees from industry and academia in 33 countries pondered the many aspects of these concerns. For example, a new multiscale approach to process design is being pursued by industry, which is also exploring the concept of microscale plants. Researchers are employing surface science to acquire the fundamental information for commercial catalytic reaction schemes. Others are coming to grips with the intractable compositional description of petroleum. ...

This work proposes a general iterative method to synthesize non-blocking supervisors for a class of generalized Petri nets (GPN), called G-systems, modeling flexible manufacturing systems (FMS). When given an uncontrolled G-system prone to deadlocks, an iterative deadlock control method is presented by utilizing reachability graph (RG) analysis. Meanwhile, the concept of a global idle place (GIP) for an original uncontrolled net system is developed. At each monitor computation step, a GIP is introduced to simplify the RG computation of the uncontrolled net system. The GIP will be finally removed when the non-blockingness of the controlled G-system is achieved. The proposed method in general provides an optimal or a near-optimal solution of the non-blocking supervisor. Finally, an illustrative G-system is utilized to verify the applicability and the effectiveness of the proposed method.

In this paper we obtain some theoretical results about iterative methods with memory for nonlinear equations. The class of algorithms we consider focus on incorporating memory without increasing the computational cost of the algorithm. This class uses for the predictor step of each iteration a quantity that has already been calculated in the previous iteration, typically the quantity governing the slope from the previous corrector step. In this way we do not introduce any extra computation, and more importantly, we avoid new function evaluations, allowing us to obtain high‐order iterative methods in a simple way. A specific class of methods of this type is introduced, and we prove the convergence order is 2n+2n−2 with n+1 function evaluations. An exhaustive efficiency study is performed to show the competitiveness of these methods. Finally, we test some specific examples and explore the effect that this predictor may have on the convergence set by setting a dynamical study.

Iteration–discretization methods for variational inequalities over fixed point sets

Introduction The chemical reactor is the heart of most industrial processes, although the reactor may represent only about 10 percent of the total capital cost; this is because the output from the reactor defines everything else that must be done downstream, particularly the separation processes. Similarly, if we wish to think about cellular rather than industrial processes, it is the chemical reactions that enable the cell or the organ to carry out its essential functions. We saw in Chapters 7 through 9 how a chemical reactor is integrated into simple processes, and we explored economic issues such as the trade-off between capital and operating costs. That discussion was limited, however, because we assumed in every case that the rate constants were fixed numbers. In doing so, we ignored one of the “handles” that the chemical engineer – or, in the case of an organism, evolution – has available to promote efficiency. Chemical reaction rates are highly temperature dependent, and precise temperature control can be critical in both the design and functioning of a reactor. Chemical reaction engineering is a broad subject, and it typically occupies at least one full course in an undergraduate chemical engineering curriculum. We introduce some basic ideas here for completeness, but we are only touching on one of the foundations of the chemical engineering profession. We restrict ourselves throughout this chapter to liquid systems, as before, in order to simplify some of the analysis while retaining the essential features, and we address only well-mixed reactor configurations.

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This book serves as an introduction to the subject, giving readers the tools to solve real-world chemical reaction engineering problems. It features a section of fully solved examples as well as end of chapter problems. It includes coverage of catalyst characterization and its impact on kinetics and reactor modeling. Each chapter presents simple ideas and concepts which build towards more complex and realistic cases and situations. Introduces an in-depth kinetics analysis Features well developed sections on the major topics of catalysts, kinetics, reactor design, and modeling Includes a chapter that showcases a fully worked out example detailing a typical problem that is faced when performing laboratory work Offers end of chapter problems and a solutions manual for adopting professors Aimed at advanced chemical engineering undergraduates and graduate students taking chemical reaction engineering courses as well as chemical engineering professionals, this textbook provides the knowledge to tackle real problems within the industry.

Chapter 2: An Introduction to Genetic Algorithms for Engineering Applications Chapter 3: Memetic Algorithms Chapter 4: Scatter Search and Path Relinking: Foundations and Advanced Designs Chapter 5: Ant Colony Optimization Chapter 6: Differential Evolution Chapter 7: SOMA-Self-Organizing Migrating Algorithm Chapter 8: Discrete Particle Swarm Optimization:Illustrated by the Traveling Salesman Problem

A New Interdisciplinary Engineering Course on Nanoscale Transport Phenomena A new interdisciplinary engineering course, “Nanoscale Transport Phenomena forManufacturing Nanodevices”, was recently developed. This course focuses on the principles ofnanoscale transport phenomena needed for manufacturing nanodevices and aims to close a largegap between nanoscience and commercial production of nanotechnology products. The coursealso helps to integrate the interdisciplinary knowledge required for designing and manufacturingnanodevices into undergraduate curricula. To meet these unique needs and challenges, fiveinstructors from three engineering departments (Chemical Engineering, Mechanical Engineering,and Plastics Engineering) have created this interdisciplinary course. This course was offered forthe first time as an elective to seniors during the 2011 fall semester and was offered again in the2012 fall semester. The course was presented through lectures, hands-on laboratory exercises,demonstration experiments, and a final design project. In this paper, we discuss the lecture topicsand eight hands-on laboratory experiments that were developed into modules to complementlectures in fluid mechanics, heat transfer, mixing, reaction engineering, electroosmosis,electophoresis, and manufacturing methods for micro and nanoscale devices. We also show thefinal project designs for the nanodevices or nanosystems that were finished by student teams atthe end of the course. Finally, we show the assessment results from the pre-post student surveysas well as faculty interviews. This new interdisciplinary course will better prepare undergraduates for employment focusedon designing and manufacturing nano/microfluidic systems, lab-on-a-chip devices, electronicsdevices, medical devices, and other emerging technologies. The impact of this senior-levelcourse will significantly enhance the “Nanomaterials Engineering Option” in the ChemicalEngineering Department undergraduate curriculum as well as the medical device industry focusin the Plastics Engineering Department, and can be used in the accelerated BS-MS programwhich is popular in the College of Engineering. The course will be available to the chemical,mechanical, and plastics engineering seniors each year. Our lab modules can be exported tofreshman introductory engineering courses in the College of Engineering. In addition, themicroscale fluid mechanics and heat transfer experiments may be incorporated into theundergraduate chemical engineering Unit Operations Laboratory courses.

In times of educational disruption, significant advances in adopting digitalization strategies have been accelerated. In this transformation climate, engineers should be adequately educated to face the challenges and acquire the new skills imposed by Industry 4.0. Among these, one of the most highly requested tools is Python. To tackle these aspects, this work establishes a pedagogical framework to teach Python to chemical engineers. This is achieved through a hands-on series of Python courses (sPyCE), covering topics as chemical reaction engineering and machine learning. Part of the series has been embedded in the curriculum of a Bachelor’s-level course at the Technical University of Denmark (DTU). Overall, students found the course to be useful; using Python, they solved systems of differential equations, mass and energy balances, set stoichiometric tables, regressions, simulations and more. Motivated by the large applicability and relevance of the covered topics, sPyCE is made publicly available on GitHub.

Extending the knowledge base of chemical engineering

Process Intensification (PI) is a modern trend in Chemical Reaction Engineering (CRE) science [...]