Control System Design in Distillation Processes and its Dependence of the Original Process Design

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.

Computer based design and analysis of super-critical extraction processes is performed within the context of choice and need of computational tools, consistency and accuracy of simulation results and importance of the need to study the conditions of operation of the process. Brief descriptions of the computational tools employed in this work are given. Numerical results demonstrating the correct and consistent use of the thermodynamic models in design, steady-state simulations and dynamic simulations are presented for two super-critical extraction processes whose details have been obtained from the open literature. The sensitivity of the conditions of operation on the phase stability of the streams which may cause operability problems are also illustrated for the two supercritical extraction processes. Finally, with a simple example, the scope for the reduction of energy utilization in super-critical extraction processes is demonstrated.

A Tabu search-based algorithm for mixed-integer nonlinear problems and its application to integrated process and control system design

This work extends the concept of relative normalized gain array (RNGA) and proposes a systematic approach to designing decentralized proportional−integral−derivative (PID) control for multi-input−multi-output (MIMO) processes containing integrators and/or differentiators. By determining the input−output-pairing using the relative gain array (RGA)−Nederlinski index (NI)−RNGA criterion, equivalent transfer functions (ETFs) for the selected input−output pairs are derived by utilizing the RGA and RNGA information, which has incorporated the information on loop interactions. Based on the ETFs, decentralized PID controllers are tuned to stabilize the MIMO system independently. The proposed approach is simple, easy to understand, and easy to implement by field engineers. Three industrial MIMO processes with different dimensions and interaction modes are employed to demonstrate the efficiency of the proposed approach.

Is the Relative Gain Array a Sensitivity Measure?

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

ProCACD: A computer-aided versatile tool for process control

This paper on the dynamics of multicomponent distillation is based on experimental investigations in a laboratory-scale distillation column. The concentration and temperature profiles are obtained at the steady-state operating point, and the transition behavior is observed by systematically changing the relevant operating variables. The general scope of these experiments is to evaluate and study the concentration and temperature profiles measured along the column height. The developed methodology for sampling at vacuum is explained. The experimental results are compared with theoretical calculations obtained by means of steady-state and dynamic simulation. The influence and the important of heat losses on the steady-state and dynamic behavior at high temperature levels are investigated and discussed. The aim of these studies is to prove the steady-state and dynamic simulation tool on the basis of the equilibrium stage model. It is found that the simulation results agree closely with those obtained experimentally. This refers to the concentration and temperature profiles as well as to the calculated and the experimentally used reboiler heat input. In order to achieve this good agreement, heat losses along the column height have to be taken into account in the simulation. Consequently, the consideration of heat losses is of great importance formore » the determination of HETP values in packed columns.« less

We report on the control of three types of dividing wall columns: columns with the dividing wall in the lower (DWCL), middle (DWCM), and upper (DWCU) portions of the column. The relative gain array (RGA) and condition number (CN) for these three column types are calculated for two different multicomponent systems. Based on these results, promising control structures are identified and tested in Aspen Dynamics. The results indicate that DWCU has the worst control performance of the three column types. DWCL outperforms DWCM in most cases, but the difference in controllability is not likely to justify the choice of a column configuration with higher costs.

System or process identification is a mathematical modeling of systems (processes) from test or experimental data. Process models obtained from identification process can be used for operator training simulator, analysis and design of safety systems, and design of process and control systems. This paper presents the Linear Parameter Varying (LPV) model identification for Industrial boiler furnace. LPV model is the development of linear time invariant models of different operating conditions along the overall operating trajectory and interpolation of linear models. The LPV model is adopted by considering the fact that boiler furnace in the thermal power plant has several operating conditions. By assuming that on every operating condition there are parameters changes, the LPV model is suitable for covering all operating conditions. The boiler furnace is modeled as LPV systems with Linear transfer function model structure. Identification algorithm used in the identification process is Prediction error method. Data needed for identification is taken from first principle model of the process with sampling time of 1 second. The identification result is simulated and validated with the measured data. The simulation result shows better accuracy for Linear Parameter Varying model.

Quantitative risk assessment integrated with dynamic process simulation for reactor section in heavy oil desulfurization process

The design of the multi-component distillation column for processing of Bonny light crude is presented using advanced process simulation software (Aspen Hysys). The steady state design models were developed from Mesh equations and were used to obtain the design parameters based on the principle of conservation of mass and energy. The design parameters were column diameter, column cross sectional area, height of the column, downcomer area, hole area, weir length, wet area and tray spacing. The equations developed are capable of predicting compositions, partial pressures and temperature of the components of interest from the mixtures of crude oil. The accuracy of the design parameters were ascertained by comparing predicted results with literature data of a distillation unit. The simulation of the design models were performed using Aspen Hysys to obtain optimum values of the most significant variables/parameters (column diameters 1.558m, column height 17.048m, cross section area 1.907m2, downcomer area 0.229m2, tray spacing 0.5m, weir length 1.200m, hole area 0.191m2 and wet area 1.678m2). The result obtained from the steady state simulation shows that the feed flow rate, temperature and pressure influence the efficiency of the distillation column.

This paper is a short introduction in how to use the dynsim R package for calculating dynamic simulations with autoregressive time series using the approach laid out in Williams and Whitten (2012). Building on the Zelig package, dynsim depicts long-run simulations of dynamic processes for a variety of substantively interesting scenarios, with and without the presence of exogenous shocks. The package includes the dynsim function for calculating the dynamic simulations and provides a function (dynsimGG) for graphically depicting the dynamic simulations with the ggplot2 package.

The Petlyuk distillation configuration has received considerable attention because of its efficiency to reduce the energy required for the separation of ternary mixtures. The dynamic properties of fully thermal coupling distillation column, which separates ternary mixtures of H2O, ethylene glycol (EG), diethylene glycol (DEG), are analyzed. The possible control configurations were investigated using Relative Gain Array (RGA) Analysis and dynamic simulation. The results indicate that the provided control scheme is able to produce a stable control response for thermally coupled distillation systems and shows better dynamic responses than the conventional distillation sequence.

Digital Twin (DT) technology has rapidly matured from pilot projects to integral components of advanced asset management and process optimization in the oil and gas (O&G) industry. This review provides a structured synthesis of the current state of digital twin frameworks, with a focus on offshore and topside gas-processing systems, such as those found on Floating Production Storage and Offloading (FPSO). Emphasis is placed on high-fidelity process simulations and scalable architectures integrating real-time data with advanced analytics. Drawing on over 85 peer-reviewed sources and industrial frameworks, the paper outlines modular DT architectures, encompassing steady-state and dynamic process simulations (e.g., Aspen HYSYS), reduced-order and hybrid machine learning models, co-simulation environments, and advanced equation-of-state packages (e.g., GERG-2008). Special attention is given to compressor map integration, Equations of State (EOS) selection, ISO/IEC standard compliance, and digital thread continuity. Additionally, the review explores economic and sustainability-driven DT implementations, including flare and methane mitigation, ISO 50001-aligned energy optimization, and lifecycle/decommissioning strategies. It concludes with a technical and economic assessment of DT maturity for gas compression facilities, identifying research gaps in standardization, long-term validation, and cybersecurity integration. The insights provided are intended to support decision-makers, engineers, and researchers in deploying scalable, auditable, and high-impact DT solutions across the O&G value chain.