Integrating dynamic process simulation into detailed automation engineering

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 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

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.

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

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

In Sweden, phosphorus is commonly removed from municipal wastewater treatment by chemical precipitation (CP). Recently, such alternatives as enhanced biological phosphorus removal (EBPR) have garnered interest due to the increased risk of chemical shortage. In this study, a life cycle assessment (LCA) was performed to compare EBPR and CP in three scenarios: 1) baseline – precipitation chemicals available, 2) stricter effluent requirements – precipitation chemicals available, and 3) chemical shortage – no precipitation chemicals available. Data acquisition that was based on dynamic process simulation was useful, yielding more site-specific results, in contrast to standard literature values. The results indicated substantial differences in greenhouse gas emissions between configurations (around three times higher methane emissions for EBPR compared to CP configurations – although this finding requires further validation). These differences suggest that different emission factors for EBPR and CP should be considered. Furthermore, it is suggested to include waterline methane emissions, at least when the configuration incorporates anaerobic reactors in the water line. Further validation of emissions is necessary, especially for EBPR plants with side-stream hydrolysis and digester reject water treatment. The LCA results showed a similar overall environmental impact for both configurations, but the results of individual impact categories differed. EBPR caused greater climate impact due to the larger direct emissions of methane. Toxicity was more important for CP, based on the inherent heavy metal content in precipitation chemicals. Freshwater eutrophication was similar for both configurations, assuming that precipitation chemicals were available. However, if the recipient is sensitive, implementing EBPR reduces the freshwater eutrophication potential by 75% during a chemical shortage, and should be considered.

The integration of offshore wind energy into Power‐to‐X (PtX) process chains offers opportunities for the efficient use of renewable energy. This article analyzes different PtX process chain configurations and their adaptation to the offshore environment. However, direct coupling of PtX platforms with fluctuating electrical energy poses major challenges. Dynamic process simulation is presented for analysis of different plant configurations and operating strategies. The article emphasizes the need for interdisciplinary research to consider technological as well as economic and environmental aspects.

Global statistical predictor model for characteristic adsorption energy of organic vapors–solid interaction: Use in dynamic process simulation

Simulation of Railway Vehicle Dynamics in Universal Mechanism Software

Dynamic Process and Validation of a Full Localization 250W@4.5K Large Scale Helium Cryogenic Plants

Zinc electrowinning is the final aqueous processing step in the production of metallic zinc. It is a complex process that is difficult to control and monitor. This thesis seeks to develop a time transient process simulation of zinc electrowinning to aid in controlling and monitoring. Based on reported models of zinc electrowinning, and other electrowinning processes, a dynamic simulation of a zinc electrowinning cell capable of representing time-based effects was developed. The new model incorporates more sophisticated estimations of electrolyte activity using Pitzer parameters. Further, recently determined empirical relations for solution conductivity, density and viscosity were included. The effect of gas evolution on the conductivity of solution was also included. First, the simulation was validated using a reported data set, and the kinetic constants of the primary cathodic reactions were determined using a simplex optimization algorithm. A model of a counter-flow cooling tower was also developed in order to simulate a full cell house. The simulation was then successfully validated against data collected from three industrial cell houses in Canada. Once validated, the simulation was used to investigate its possible applications. The benefits of optimizing a single electrowinning cell in comparison to optimizing the cell house was examined. Due to the inability to directly control both the acid and zinc concentrations in the electrowinning cell without major process changes, it was established that the optimal single cell conditions favoured zinc concentrations of approximately 70 g L‐¹, while the cell house optimization favoured lower zinc concentrations by comparison (approximately 50 g L‐¹). The simulation was used to track the consumption of manganese ions in the cell house when using variable concentrations of manganese in the purified electrolyte fed to the cell house. The experimental rates reported for Mn consumption overestimated the Mn consumption in a cell house. The simulation was also used to investigate process control strategies. With the use of a basic process control scheme, the production could increase by 4 % and decrease the variability in the spent electrolyte concentration.

Comparison of process concepts for preparative chromatography