Author

# D.E. Haskins

Bio: D.E. Haskins is an academic researcher from Humble Oil. The author has contributed to research in topic(s): Residence time distribution & Plug flow reactor model. The author has an hindex of 1, co-authored 1 publication(s) receiving 21 citation(s).

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Humble Oil

^{1}TL;DR: In this paper, the problem of determining the optimal reactor type as dictated by reaction kinetics is considered, and the problem is formulated such that the axial dispersion coefficient is chosen as a function of reactor residence time to optimize reactor performance.

Abstract: The determination of the optimal reactor type as dictated by reaction kinetics is considered. It is assumed thay any reactor type can be described with an axial dispersion model. The problem is formulated such that the axial dispersion coefficient is chosen as a function of reactor residence time to optimize reactor performance. This corresponds physically to the design engineer picking the best type of reactor to combination of reactors. Another important assumption made is that the reaction kinetics can be described with a pseudo-homogenous rate expression(s). This assumption makes it possible to find the broad categories of reactor type, stirred tank and/or plug flow, but not detailed reactors such as fluidized or packed bed. The method presented here handles arbitrary reaction kinetics and a very broad class of objective functions which might involve selectivities, yields, operating costs, combinations of these, or other considerations. The reactor selection is cast into form of an optimal control problem and a first order gradient method is used to solve the iterative optimization calculations. A simple auto-catalytic reaction is used to demonstrate this method. Results for other kinetic systems are discussed.

21 citations

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TL;DR: In this paper, the synthesis problem of complex nonisothermal reaction networks is addressed, where the authors focus on developing a superstructure of alternatives for the non-isothermal reactor system, and subsequently formulate and solve the problem according to the proposed general structure.

Abstract: In this paper, the synthesis problem of complex nonisothermal reactor networks is addressed. The focus is on developing a superstructure of alternatives for the nonisothermal reactor system, and subsequently formulate and solve the synthesis problem according to the proposed general structure. The nonisothermal case features alternatives related to the type of temperature control for the reactors and includes options for directly or indirectly intercooled or interheated reactors. The approach is applicable to any homogeneous exothermic or endothermic complex reaction and the synthesis problem, formulated as a mixed integer nonlinear programming (MINLP) problem, can handle both thermodynamic and economic objective functions. The solution of the resulting MINLP problem provides information about the optimal type of temperature control, the optimal temperature profile, the feeding, recycling and bypassing strategy as well as the optimal type and size of the reactor units (CSTR, PFR) to be used. The efficiency of the method is illustrated with three different examples considering complex nonisothermal reaction mechanisms.

177 citations

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TL;DR: In this paper, a nonlinear programming (NLP) formulation for optimally generating reactor networks that would produce the desired effects, given a kinetic mechanism and expressions for the reaction rate is presented.

Abstract: Frequently the product mix in a reacting system is influenced by both heat and mixing effects in the reactor. This paper presents a nonlinear programming (NLP) formulation for optimally generating reactor networks that would produce the desired effects, given a kinetic mechanism and expressions for the reaction rate. Using a generalized configuration of recycle reactors and heat exchangers (superstructure) as a basis, the NLP is able to extract the optimal subnetwork of units that would maximize an arbitrary objective function defined over a given kinetic scheme. Thus mixing and heat effects that affect reactions occurring in the homogeneous phase are taken into account. The resulting model and adjoint equations of this formulation form a two-point boundary value problem that interfaces with an efficient optimization strategy. Decisions representing network structure, reactor type and the amount of heat addition are made through continuous parameters in the model. The superstructure allows for serial and parallel connections involving reactor units, and is therefore fairly general. Since the method is equation based and since there are numerous ODE solvers it can be applied to large kinetic mechanisms with almost any objective function. Literature examples are presented and solved in order to demonstrate the effectiveness of this approach.

92 citations

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TL;DR: In this paper, the problem of nuclear network synthesis is addressed, which involves determining the type, size, and interconnections of the reactor units, optimal concentration and temperature profiles, and the heat load requirements.

Abstract: The reactor network synthesis problem involves determining the type, size, and interconnections of the reactor units, optimal concentration and temperature profiles, and the heat load requirements ...

58 citations

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TL;DR: In this article, a two-level stochastic optimization approach is proposed to target the performance of chemical reactors with the use of stochastically optimized configurations. But it is not restricted by the dimensionality or the size of the problem.

Abstract: A systematic methodology to target the performance of chemical reactors with the use of stochastic optimization is presented. The approach employs a two-level strategy where targets are followed by the proposition of reactor configurations that match or are near the desired performance. The targets can be used for synthesis and retrofit problems, as they can provide the incentives to modify the operation, and ideas in developing the reactor design. The application of stochastic optimization enables confidence in the optimization results, can afford particularly nonlinear reactor models, and is not restricted by the dimensionality or the size of the problem.

57 citations

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TL;DR: In this article, the authors used the attainable region (AR) technique to solve the problem of particle breakage, separation, and mixing in the comminution process of particle size distribution (PSD).

Abstract: The aim of any comminution circuit is to produce material of a desired particle size distribution (PSD) at a minimum operational cost. Currently, the comminution process is energy intensive and operates at very low efficiency when the input energy is compared to the breakage achieved. The attainable region (AR) technique has been successfully used to solve optimization problems simultaneously with the process synthesis formulation of reactor systems. The AR looks at the fundamental processes of a given system and determines all the possible outputs to which the objective function can be applied and an optimal process solution selected. Particle breakage, separation (classification) and mixing are identified as the three fundamental processes of interest taking place during comminution. Breakage and mixing processes are used in this paper to illustrate the applicability of the AR theory in comminution. We develop a fundamentally based model which is equipment independent to describe breakage. Specific energy is the independent variable and the production of particles with a certain PSD is the objective function. We use geometric construction to represent this PSD as a point in an n -dimensional space in relation to an input specific energy. Output PSDs are dependent on the input PSDs, allowing connectivity of the batch grinding stages to form a pseudo-continuous process. Specific energy is used as the control variable to obtain sharper product PSDs. It is shown that the same net energy consumed in the system can produce different product PSDs. Therefore, this implies that the design of comminution circuits should achieve better control of the specific energy. Once the candidate AR is constructed, operational process targets can be defined more accurately. This establishment of targets permits a measure of the actual process efficiency against a theoretical target. The advantage of the AR method lies in its ability to develop not only the performance of the optimal circuit but also the operational conditions to be used in the optimal process circuit. This also answers the process synthesis question of the type of equipment to be used which is a function of the specific energy.

44 citations