Performance Analysis of Perturbation‐Based Methods for Real‐Time Optimization

This paper provides a comprehensive performance analysis approach for Real‐Time Optimization (RTO) technologies, which incorporates systematic approaches to estimating bounds on the convergence behaviour and performance effects of on‐line experiments used by a given RTO approach. The performance analysis method is illustrated by an investigation of the conventional two‐phase approach and representative techniques drawn from the three main classes of perturbation‐based RTO methods which attempt to directly compensate for plant/model mismatch through adaptation. The proposed approach is applied to two simulation‐based case studies: a heat exchanger system and a continuous bioreactor.     

Development of an Electrical Resistance Tomography Reactor for Pharmaceutical Processes

A review of tomography systems suitable for industrial implementation and design considerations for tomographic sensors has allowed the design of an ERT sensor compatible with stringent process requirements. In conjunction with a data acquisition system, ITS P2000, this sensor was applied to processes considered typical of Active Pharmaceutical Ingredients (API) chemical development. Experimental results are described where variations in conductivity measurements were monitored in a stirred tank. The data were compared with spectroscopic on‐line monitoring and kinetic information. The results obtained demonstrate that this approach shows promise for on‐line control of mixing process performance and efficiency evaluation and optimisation of reactor geometries.     

Design‐based life cycle assessment of hazardous air pollutant control options at pulp and paper mills: a comparison of thermal oxidation to photocatalytic oxidation and biofiltration

BACKGROUND: The forest products industry produces valuable industrial chemicals, wood products, and consumer goods, but is also responsible for the emission of significant quantities of hazardous air pollutants. Although many air pollution control options are available, little is known about the overall environmental impacts of implementing each option. Therefore, a life cycle assessment (LCA) was conducted to compare energy and raw material inputs, air emissions, and environmental impacts associated with construction and operation of two air pollution control systems: regenerative thermal oxidation (RTO) with wet scrubbing and photocatalytic oxidation (PCO) with biofiltration. RESULTS: LCA results indicated that environmental impacts to resource depletion, photochemical oxidant formation, and acidification were 20% higher for the use of a RTO‐scrubber than for the PCO‐biofilter. In addition, at least 25% of the RTO impacts were due to infrastructure requirements. However, the PCO‐biofilter system was responsible for more environmental impact in categories of global warming and human toxicity, because of the packing materials required and the electricity use for the PCO reactor. CONCLUSIONS: The PCO‐biofilter system could be a promising, environmentally‐friendly alternative to traditional RTO devices, provided that this system is modified to decrease resource and energy demands. Copyright © 2008 Society of Chemical Industry     

Modelling and optimisation of a chemical industry wastewater treatment plant subjected to varying production schedules

Industrial wastewater treatment plants (WWTPs) have to provide 100% reliability and availability for the discharging facilities at an industrial site. Varying production schedules at these facilities and specific components occurring in the industrial wastewater considerably hinder the optimisation of industrial WWTPs. In this context it is shown in this paper that model‐based optimisation is an efficient and cost‐reducing way to ensure that an industrial WWTP functions well. The aim of the study presented was two‐fold. The first step was to show the usefulness of a proposed procedure to build and calibrate a model for the industrial WWTP. The second objective was to use the model for optimisation of the WWTP. As an example, a large set of possible production schedules in the different discharging facilities was simulated. Based on these simulations it could be predicted which schedules allow the effluent standards to be met and which do not. The calibrated and validated model was also used to investigate different operating strategies such as the in‐series operation of the two available aeration tanks. In fact, with the model it was shown that a 20% reduction of the degradable COD concentration in the effluent could be achieved by operating the tanks in series instead of in parallel. This case study shows how the approach presented can lead to fast and cost effective modelling and optimisation of an industrial WWTP. Copyright © 2004 Society of Chemical Industry     

Sample‐based approaches to decision making problems under uncertainty

Decision making under uncertainty is becoming more important in process industries as optimisation is applied to novel applications as well as plant‐wide and enterprise optimisation. Among the standard stochastic optimisation techniques are stochastic programming and dynamic programming. It is difficult to use these techniques for practical applications due to unwieldy computational requirements, arising from a large number of uncertain parameters and state variables, respectively. In this paper, we present sample‐based techniques for ameliorating the computational difficulties. Application studies involving catalyst design and real‐time optimisation point to the promising potentials of the sample‐based techniques. © 2011 Canadian Society for Chemical Engineering     

Multi-period design of heat exchanger networks

Heat exchanger networks are an integral part of chemical processes as they recover available heat and reduce utility consumption, thereby improving the overall economics of an industrial plant. This paper focuses on heat exchanger network design for multi-period operation wherein the operating conditions of a process may vary with time. A typical example is the hydrotreating process in petroleum refineries where the operators increase reactor temperature to compensate for catalyst deactivation. Superstructure based multi-period models for heat exchanger network design have been proposed previously employing deterministic optimisation algorithms, e.g. (0005 and 0180). Stochastic optimisation algorithms have also been applied for the design of flexible heat exchanger networks recently (0110 and 0115). The present work develops an optimisation approach using simulated annealing for design of heat exchanger networks for multi-period operation. A comparison of the new optimisation approach with previous deterministic optimisation based design approaches is presented to illustrate the utilisation of simulated annealing in design of optimal heat exchanger network configurations for multi-period operation.     

Prozesswärmeerzeugung im heißen Bypass von RTO‐Anlagen versus separater Wärmeerzeugung

Plants for regenerative thermal oxidation (RTO) are used for detoxification of solvent‐containing exhaust gases. The achieved exhaust gas preheating temperatures enable over‐autothermal operation with only relatively low solvent loadings, so that a process heat decoupling can be implemented without additional fuel. If there is not enough process heat generated, the peak demand is usually generated by a direct‐fired boiler. It is demonstrated that it can be energetically favorable to generate the process heat only by the RTO – even by use of additional fuel.     

An overview of the mathematical modelling of liquid membrane separation processes in hollow fibre contactors

Liquid membranes have traditionally been employed for liquid/liquid mass transfer and have found applications in industrial, biomedical and analytical fields as well as in hydrometallurgical processes, wastewater treatment and remediation of polluted groundwater. However, in spite of the known advantages of liquid membranes, there are few examples of industrial application. The development of reliable mathematical models and design parameters (mass transport coefficients and equilibrium or kinetic parameters associated with the interfacial reactions) is a necessary step for design, cost estimation, process optimisation and scale‐up. This work reports an overview of the different approaches that have been proposed in the literature to the mathematical modelling of liquid membrane separation processes in hollow fibre contactors providing, at the same time, a useful guideline to characterise the mass transport phenomena and a tool for the optimal design and intensification of separation processes. Copyright © 2009 Society of Chemical Industry     

Dynamic Matrix Control of a Bubble‐Column Reactor for Microbial Synthesis Gas Fermentation

A spatiotemporal metabolic model of a representative syngas bubble‐column reactor was applied to design and evaluate dynamic matrix control (DMC) schemes for regulation of the desired by‐product ethanol and the undesired by‐product acetate. This model was used to develop linear step response models for controller design and also served as the process in closed‐loop simulations. A 2 × 2 DMC scheme with manipulation of the liquid and gas feed flows to the column provided a superior performance to proportional integral (PI) control due to slow process dynamics combining the multivariable and constrained nature of the control problem. Ethanol concentration control for large disturbances was further improved by adding the flow of a pure hydrogen stream as a third manipulated variable. The advantages of DMC for syngas bubble‐column reactor control are demonstrated and a design strategy for future industrial applications is provided.     

Optimising mixing and nutrient removal in membrane bioreactors: CFD modelling and experimental validation

Membrane Bioreactors (MBRs) have been used successfully in biological wastewater treatment to solve the perennial problem of effective solids-liquid separation. The optimisation of MBRs requires knowledge of the membrane fouling, mixing and biokinetics. MBRs are designed mainly based on the biokinetic and membrane fouling considerations even though the hydrodynamics within an MBR system is of critical importance to the performance of the system. Current methods of design for a desired flow regime within the MBR are largely based on empirical techniques (e.g. specific mixing energy). However, it is difficult to predict how vessel design in large scale installations (e.g. size and position of inlets, baffles or membrane orientation) affects hydrodynamics, hence overall performance. Computational Fluid Dynamics (CFD) provides a method for prediction of how vessel features and mixing energy usage affect the hydrodynamics and pollutant removal and subsequently allowing optimisation of MBR design and performance. In this study, a CFD model was developed which accounts for aeration and biological nutrient removal. The modelling results are compared against experimental results of two full scale MBRs for the hydrodynamics and against a modelling benchmark for the biological nutrient removal component of the model.     

Real-time optimization for a laboratory-scale flotation column

In this paper, a supervisory layer with real-time optimization (RTO) has been implemented in an experimental laboratory-scale flotation column for copper concentration. A two-stage and modifier adaptation (MA) methodology for RTO has been compared under structural, experimental and dynamic uncertainty. In addition, a gradient-free alternative for MA, called nested modifier optimization, has been proposed and tested. The results show that the KKT updates of the MA approach allow the process optimum to be determined under uncertain scenarios, unlike the two-stage approach. From the perspective of gradient modifiers, the performance of the nested methodology is comparable to the dual approach because previous past values are used to update the modifiers without requiring the gradient estimation step. In addition, the interaction of RTO with the regulatory layer must be considered to propose an optimal implementation.     

Study of the bio‐production of carotenoids by Sporidiobolus salmonicolor (CBS 2636) using pre‐treated agro‐industrial substrates

BACKGROUND: The increasing industrial demand for carotenoids has aroused interest in their bio‐production, and the need to reduce production costs has encouraged the use of low cost industrial substrates, such as agro‐industrial residues. Thus the objective of this research was the bio‐production of carotenoids by Sporidiobolus salmonicolor using agro‐industrial substrates (corn steep liquor and sugarcane molasses), pre‐treated with acids (sulphuric and phosphoric). RESULTS: Bio‐production was carried out in an orbital shaker using a 10% (v/v) inoculum, incubation at 25 °C, and agitation at 180 rpm for 120 h in a non‐illuminated environment. The carotenoids were recovered using liquid N₂ combined with dimethylsulphoxide for cell rupture, and an acetone/methanol mixture (7:3 v/v) for extraction. CONCLUSION: The complete second‐order design allowed for optimisation of the carotenoid concentration obtained from industrial substrates pre‐treated with acids (sulphuric and phosphoric), obtaining a total carotenoid content of 541.5 µg L^?1 using 10 g L^?1 sugarcane molasses, 5 g L^?1 corn steep liquor and 5 g L^?1 yeast hydrolysate at 25 °C, with agitation at 180 rpm and an initial pH of 4.0. Copyright © 2008 Society of Chemical Industry     

The Development and Application of a Novel Optimisation Strategy for Solid Oxide Fuel Cell‐Gas Turbine Hybrid Cycles

A combined power system with solid oxide fuel cell (SOFC) and gas turbine (GT) is modelled and analysed thermodynamically in this paper. A novel optimisation strategy including the design of optimal parameters is proposed and applied to the hybrid system. Different sources of irreversible losses are specified, and entropy analyses are used to indicate the multi‐irreversibilities existing, and to assess the work potentials of the system. Expressions of the power output and efficiency for both the subsystems and the SOFC‐GT hybrid system are derived. The optimal performance characteristics are presented and discussed in detail through a parametric analysis. The developed model is expected to provide not only a convenient tool to determine the optimal system performance and component irreversibility, but also an appropriate basis to design similar complex hybrid power plants. This new approach can be further extended to other energy conversion settings and electrochemical systems. Decision makers should therefore find the methodology contained in this paper useful in the comparison and selection of advanced heat recovery systems.     

Design and assessment of delay timer alarm systems for nonlinear chemical processes

In process and manufacturing industries, alarm systems play a critical role in ensuring safe and efficient operations. The objective of a standard industrial alarm system is to detect undesirable deviations in process variables as soon as they occur. Fault detection and diagnosis systems often need to be alerted by an industrial alarm system; however, poorly designed alarms often lead to alarm flooding and other undesirable events. In this article, we consider the problem of industrial alarm design for processes represented by stochastic nonlinear time‐series models. The alarm design for such complex processes faces three important challenges: (1) industrial processes exhibit highly nonlinear behavior; (2) state variables are not precisely known (modeling error); and (3) process signals are not necessarily Gaussian, stationary or uncorrelated. In this article, a procedure for designing a delay timer alarm configuration is proposed for the process states. The proposed design is based on minimization of the rate of false and missed alarm rates—two common performance measures for alarm systems. To ensure the alarm design is robust to any non‐stationary process behavior, an expected‐case and a worst‐case alarm designs are proposed. Finally, the efficacy of the proposed alarm design is illustrated on a non‐stationary chemical reactor problem. © 2017 American Institute of Chemical Engineers AIChE J, 63: 77–90, 2018     

基于被控变量在线建模的化工过程实时优化方法

选择合适的被控变量可对过程进行实时优化(RTO),但现有方法在设计阶段确定被控变量后,不允许对其进行在线调整,导致了RTO效果的局限性。针对这一问题,提出了一种基于被控变量在线建模的方法,使用局部建模技术在线寻找相似样本并建立一阶最优性必要条件(NCO)的估计模型,将其作为被控变量更新控制回路,在反馈控制作用下达到更好的RTO效果。对一个蒸发过程的研究表明,此方法能够通过对NCO的在线准确建模,增加生产过程的经济效益。     

A boundary value design method for complex demethaniser distillation columns

Typically the number of design options for demethaniser flowsheets, that recover methane from a gas mixture, is large. Repetitive simulations, to evaluate the economic viability of alternatives, do not usually enable a thorough exploration of the variable space for the purposes of process synthesis. More comprehensive process optimisation is facilitated by shortcut design models and a suitable optimisation framework. These optimisation results, applying shortcut models, are useful at the initial design stage, when the range of flowsheet options and operating conditions to be explored is relatively wide.A demethaniser column has many degrees of freedom, including the operating pressure, the location and the order of feeds, the number and duty of side reboilers and the flow rate of the external reflux stream. The complexity of the demethaniser column precludes the use of the Fenske-Underwood-Gilliland shortcut design method. An appropriate design model for the demethaniser is presented for application within an optimisation framework for process synthesis and evaluation. The column design model is computationally relatively undemanding, yet accurate, so should allow evaluation of both energy demand and equipment requirements.The design model presented is a semi-rigorous boundary value method for the design of complex demethaniser columns separating multicomponent mixtures. The method has been implemented within MATLAB and linked to HYSYS for prediction of physical and thermodynamic properties. Industrially relevant examples demonstrate that the results of the proposed design methodology are in good agreement with those of rigorous simulation.     

Analytical optimisation of industrial systems and applications to refineries, petrochemicals

A new method for optimising process networks is presented in this paper. The method uses economic analysis of existing systems based on the new value analysis method (Ph.D. Dissertation, UMIST, Manchester, UK, 2002) as the basis to derive the optimum network design. The analytical optimisation method comprises of three steps. Market integration is the first step that fully exploits the available market opportunities for selling and purchasing streams based on individual marginal contributions from productions and processing of streams. Market integration is an easy and straightforward way of achieving quick benefits. The second step deals with optimisation of network flowsheet/connections. The economic margins of various paths of network are used to determine the weaker paths and the stronger paths where the loads of weaker paths can be shifted. This load shifting among paths leads up to the overall benefits of a system. Finally, the non-profitable or less profitable process units are optimised to improve their individual marginal contributions. Analytical optimisation turns the traditional back box approach into a clear and transparent procedure and is simple to understand and easy to use. The application of analytical optimisation is demonstrated with industrial cases from refining. In the end, a generalised methodology has been illustrated on how to design the optimum flowsheet of a petrochemical complex in a changing market price scenario.     

Simultaneous synthesis of structural‐constrained heat exchanger networks with and without stream splits

This paper presents a comprehensive simultaneous synthesis approach based on stage‐wise superstructure to design cost‐optimal heat exchanger network (HEN). It is well known that the simultaneous synthesis model has very complicated mixed integer nonlinear programming formulations, which are non‐convex, non‐continuous and have many local optima. Up till now, it cannot be expected that an algorithm can find, in polynomial time, the global solution to the simultaneous synthesis problem of HEN. In order to reduce computational complexity, some simplified assumptions for structures, such as no stream splits, stream splits with isothermal mixing, no stream split flowing through more than one exchanger, etc, are adopted to prune the search space at the expense of neglecting certain important alternatives in the network configuration. In this work, a flexible stage‐wise superstructure is proposed to control the solution performance and search space efficiently. At each stage of the superstructure, with or without stream splits is determined at random or by the experience of designers. In this way, various candidate series and split network designs featuring the lowest annual cost can be found. Moreover, an efficient two‐level optimisation algorithm is employed for solving the presented model utilising genetic algorithm and particle swarm optimisation algorithm. Three case studies are presented to show the applicability of the proposed methodology. In addition, the results show that the new approach is able to find more economical networks than those generated by other methods. © 2012 Canadian Society for Chemical Engineering     

Assessing the Performance of Model Predictive Controllers

Performance assessment of model predictive controllers is a problem of significant industrial relevance. Model predictive controllers belong to a class of linear time‐varying controllers, which compute the future control actions by minimizing a constrained, time‐varying objective function. In this work we propose a performance statistic that takes into account the time‐varying and constrained nature of model predictive control. The proposed measure compares the achieved objective function with its design value, online. Analytical expressions are derived to calculate the expected value of the design objective function under closed loop conditions. Simulation and industrial case studies are used to illustrate the applicability of the proposed metric.     

Real-Time Optimization Using Machine Learning Models Applied to the 4,4′-Diphenylmethane Diisocyanate Production Process

In this work, the optimal time-varying allocation of steam in a large-scale industrial isocyanate production process is addressed. This is a problem that falls into the category of real-time optimization (RTO). The application of RTO in practice faces two problems: First the available rigorous process models may not be suitable for use in real-time connected to the process. Second, there is always a mismatch between the predictions of the model and the behavior of the real plant. We address the first problem by training a neural net model as a surrogate to data generated by a rigorous simulation model so that the model is simple to implement and short execution times result. The second problem is tackled by adapting the optimization problem based on measured data such that convergence to the optimal operating conditions for the real plant is achieved.