Newton-type decomposition methods in large-scale dynamic process simulation

Dynamic simulation for flare minimization in chemical process industry under abnormal operations

Parallel dynamic process simulation of a distillation column on the BBN Butterfly Parallel Processor computer

Supercritical fluid extraction (SFE) is a process in which a solute present in a homogeneous mixture is preferentially dissolved by a supercritical fluid (SCF) solvent. SCFs are gases or liquids above their critical points. Under these conditions SCFs exhibit enhanced solvating power, higher selectivities, and other desirable physicochemical properties which makes them particularly good solvents. A dynamic SFE process simulator has been developed by modeling various units of a typical SFE process, and realism of the simulated results is enhanced by accounting for instrument noise, random drifts, transport delays, and analyzer dead times. The process simulator has been compared with operating data from a pilot-scale SFE process and yields good agreement. The dynamic process simulator provides a much needed insight into various process interactions and the nonlinearities that can exist in an integrated, multiunit process

The understanding of the dynamic behavior of distillation columns has received considerable attention because distillation is one of the most widely used unit operations in chemical process industries. This paper reports a dynamic simulation study of the possible distillation columns sequences of Dew pointing unit in the second phase of South Pars Gas Refinery. In this unit, three columns are used for separating the feed of normal paraffin, from methane to n-Decane into the four mixtures of products; so five different simple columns sequences are possible. In this work, we made use of linking between Aspen dynamic and MATLAB Simulink software’s for achieving our purpose. At first, simulation and design of the distillation sequences were performed in steady state by using the process simulators Aspen Plus 2006. After steady state simulation, the parameters required for the dynamic simulation were entered and the files were exported to ASPEN Dynamics. PI and PID controller as basic controllers were automatically added and were tuned by the conservative Tyreus–Luyben tuning method. Then the model which connects MATLAB to Aspen Dynamic was created in Simulink and the behavior of the five different sequences in dynamic regime was observed after changing the flow rate of the feed steam by. The results show that the steady state simulation is suitable for the start point, but it is better to use dynamic simulation to design and simulate the chemical process industries because in dynamic simulator there are nonlinear models for calculating the equations of state and simulating the chemical process. In addition, in dynamic simulation we are faced with real condition of process so the obtained results will be close to the real ones. After dynamic investigation, it were found that the sequences-2, sequences-4 and sequences-5 have suitable dynamic behavior for controlling because of auto-rejection of the disturbances, but the sequences-1 and sequences-3 have complex dynamic response and they are found to be hard to control.

The dynamic simulation of chemical processes is an indispensable and central tool for process design, analysis, and operation. Compared to a steady state simulation, the dynamic process simulation has some outstanding advantages, such as the assessment of control system reliability before its implementation on the real plant, the planning and testing of start-up and shutdown procedures, the training of operators, the possibility to investigate the causes of previous accidents, and to determine the consequences of possible accidents. In particular, as far as the accidents are concerned, it is not of minor importance that they are an economic liability to the company and the community (Fewtrell and Hirst, 1998). Fewtrell and Hirst highlighted also that high-cost accidents have some common features. The coupled dynamic simulation of process and accidents allows identifying the aforementioned deficiencies while moving towards a more safe and productive management of chemical processes. This manuscript analyzes the possible plant/accident interactions and suggests the simultaneous dynamic integration of process simulators and accident simulators. In fact, to simulate a realistic accident dynamics, both the dynamics of the plant and of the accidental event should be run simultaneously since they are biuniquely interrelated and they influence one another. The manuscript investigates the feedbacks and interactions between the process and the accident by means of a specific case study.

In conjunction with choosing the treatment process for the new wastewater treatment plant of Espoo, Finland (400,000 P.E.), Denitrification-Nitrification (DN) and Step-Feed activated sludge processes were compared in terms of required basin volume and consumption of aeration air and methanol. The comparison was made using dynamic process simulation. The advantages of the step-feed process reported in literature – smaller volume required to treat an equal load or ability to treat a higher load in an equal volume – were questioned. In terms of consumables, the two processes were found practically equal. This is, to the best of our knowledge, the first comparison of these process configurations with dynamic simulation.

The teaching of higher mathematics is fundamentally challenged by its abstract and logical nature, while traditional instructional methods show significant limitations in visualizing and dynamizing core concepts. The rise of artificial intelligence has brought revolutionary breakthroughs to the visualization teaching of higher mathematics, largely due to its powerful capabilities in image generation, natural language interaction, and dynamic simulation. This paper focuses on the application of AI technology in this field and systematically constructs three teaching models, “intelligent diagram generation” “interactive experimental exploration”, and “dynamic process simulation”. These models are supported by in-depth analysis of typical cases such as saddle surfaces, vector fields, and integrals. Furthermore, the paper discusses the theoretical foundations and practical pathways of AI visualization teaching, aiming to provide theoretical guidance for promoting the paradigm shift in higher mathematics education from static abstraction to dynamic intuition.

Time delays in dynamic simulation

An algorithm for efficient specification analysis in large-scale dynamic process simulation

Plant engineering workflow consists of different phases and engineering disciplines. Dynamic process simulation can be utilized to mitigate risks and improve quality of design in the engineering workflow. Earlier and more extensive utilization of simulation is proposed in this paper using an integrated simulation-assisted engineering workflow. In this paper, basic automation design and tests are conducted in dynamic process simulation software Apros. The basic automation design is delivered to detailed design for implementation. Usually engineering projects utilize multiple non-interoperable software tools requiring significant manual data transfer. Standard engineering data interfaces provide reusable and automatic transfer features for engineering software. In this paper, standard-based integration of automation and simulation engineering is demonstrated by transferring automation design data in PLCopen XML format to detailed design. The integration and its benefits are demonstrated and some limitations and challenges for wider utilization are recognized.

ABSTRACTDifferential‐algebraic equation (DAE) systems present a number of difficult problems in nonlinear simulation and control. One of the key difficulties is that DAEs are not expressed in an explicit state space form required by many simulation and control design methods. In this paper, the problem is addressed using a new approach that constructs an explicit state space approximation of the DAEs using a sliding controller. The state space model can in turn be used with existing nonlinear control and simulation methods. This procedure, known as realization, is achieved by developing a boundary layer sliding controller with a dynamic sliding manifold. The approach builds on previous realization methods proposed by the author that employ a static sliding control surface. The approach is generalized by employing a dynamic sliding manifold which allows much greater freedom in determining optimality, robustness, and convergence of the realization than previous methods allow. The necessary criteria for key properties such as convergence, stability, and controllability of this new method are proven using a special type of sliding normal form. Furthermore, the important property of observability for sliding realizations is established for the first time by analyzing the convergence of local eigenvectors of the approximation. Together these results establish a new general framework for realization of a large class of nonlinear high index DAE systems.

New low-temperature FKM for effective sealing in the chemical industry

Using quantitative risk assessment in the chemical process industry

This thesis describes the design and implementation of a new dynamic simulator called DASP. It is a computer program package written in standard Fortran 77 for the dynamic analysis and simulation of chemical plants. Its main uses include the investigation of a plant's response to disturbances, the determination of the optimal ranges and sensitivities of controller settings and the simulation of the startup and shutdown of chemical plants. The design and structure of the program and a number of features incorporated into it combine to make DASP an effective tool for dynamic simulation. It is an equation-oriented dynamic simulator but the model equations describing the user's problem are generated from in-built model equation library. A combination of the structuring of the model subroutines, the concept of a unit module, and the use of the connection matrix of the problem given by the user have been exploited to achieve this objective. The Executive program has a structure similar to that of a CSSL-type simulator. DASP solves a system of differential equations coupled to nonlinear algebraic equations using an advanced mixed equation solver. The strategy used in formulating the model equations makes it possible to obtain the steady state solution of the problem using the same model equations. DASP can handle state and time events in an efficient way and this includes the modification of the flowsheet. DASP is highly portable and this has been demonstrated by running it on a number of computers with only trivial modifications. The program runs on a microcomputer with 640 kByte of memory. It is a semi-interactive program, with the bulk of all input data given in pre-prepared data files with communication with the user is via an interactive terminal. Using the features in-built in the package, the user can view or modify the values of any input data, variables and parameters in the model, and modify the structure of the flowsheet of the problem during a simulation session. The program has been demonstrated and verified using a number of example problems.

The use of modeling and simulation methods for engines is considered an important part of the aircraft design process. However, the traditional approach is complicated and time-consuming. In this work, a facile, novel engine dynamic simulation method was proposed, combining the effects of mass and energy flow accumulation based on the thermodynamics of the variable mass system. The typical twin-spool axis flow turbojet engine was selected as the simulation model and the dynamic simulation of the three-stage aircraft flight process was further carried out. The simulation results confirm that the new approach can greatly enhance simulation speed and is about 28 times faster than the traditional method. Besides simulation speed, accuracy has also been improved compared with the current simulation approaches. For example, during a 9 s acceleration process in which the Mach number increases from 0.6 to 0.8, errors of up to 0.473 s in time delay and up to 0.66% in energy were eliminated. During a 6 s acceleration process, 0.624 s of the time delay error was eliminated. This work plays a positive role in the accurate and rapid simulation of aircraft engines and, more importantly, lays the foundation for the simulation of other systems involving mass and energy flow.