Process Performance Criteria

In order to evaluate and to compare different process options, performance criteria or figures of merit have been developed in the domain of electrochemical process engineering. For the reaction described in Equation (17.1) performed in a continuous fiow reactor in steady-state fiow, the reagent conversion 0, product selectivity a and material yield / are defined by 

Current efficiencies below unity indicate either that to some extent the back reaction occurs on the counter-electrode, or, more likely, that undesired products are being formed. These may, however, arise by electrolysis of the solvent or the carrying electrolyte. Hence they are not necessarily associated with a material yield lower than unity. 

One of the most valuable statements of reactor performance is the space-time yield, vhich expresses the mass amount of product per unit of time and reactor volume Vr  

It is therefore an important challenge to electrochemical engineers to design electrolytic cells characterized by high mass transfer coefficients and important specific electrode area. 

The specific energy consumption states the electric power required to make unit weight of the product. It is a function of both the electrolysis conditions and the cell design. The specific energy consumption is given by [2] 

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Jacobsen s five criteria were picked for a laboratory situation in which a synthetic target compound, sometimes not even optimized for its purpose, was needed quickly and probably just once or at best a few times, and on a laboratory scale. The criteria were not developed for the situation of large-scale, continual production of a narrowly defined but well-known product. One-time laboratory synthesis protocols have to be concerned about instant availability and instant cost of catalysts (usually not under the influence of the user) but re-usability of catalyst and process performance criteria such as volumetric productivity are not and do not have to be considered. For large-scale continual production the situation is often reversed. 

Control of pollutants by oxidation is another exothermic process in which high conversion is the most important performance criterion. Interest in efficiency is limited to minimize byproduct formation the byproducts can be more damaging and more refractory than the original pollutants were. Commercially, most adiabatic reactors used for pollution control are of the least expensive construction. 

Optimization pervades the fields of science, engineering, and business. In physics, many different optimal principles have been enunciated, describing natural phenomena in the fields of optics and classical mechanics. The field of statistics treats various principles termed maximum likelihood, minimum loss, and least squares, and business makes use of maximum profit, minimum cost, maximum use of resources, minimum effort, in its efforts to increase profits. A typical engineering problem can be posed as follows A process can be represented by some equations or perhaps solely by experimental data. You have a single performance criterion in mind such as minimum cost. The goal of optimization is to find the values of the variables in the process that yield the best value of the performance criterion. A trade-off usually exists between capital and operating costs. The described factors—process or model and the performance criterion—constitute the optimization problem. ... 

An optimization problem is a mathematical model which in addition to the aforementioned elements contains one or multiple performance criteria. The performance criterion is denoted as objective function, and it can be the minimization of cost, the maximization of profit or yield of a process for instance. If we have multiple performance criteria then the problem is classified as multi-objective optimization problem. A well defined optimization problem features a number of variables greater than the number of equality constraints, which implies that there exist degrees of freedom upon which we optimize. If the number of variables equals the number of equality constraints, then the optimization problem reduces to a solution of nonlinear systems of equations with additional inequality constraints. 

The main criterion to be considered is the scale of the project, which distinguishes between large or production scale (kg a 1 to ta 1) and small or laboratory scale (mg a-1 to ga"1). This implies the question of whether the separation justifies a time-consuming method development and process design to improve process performance in terms of productivity, eluent consumption and yield. 

The shape of the complete closed-loop response, from time t = 0 until steady state has been reached, could be used for the formulation of a dynamic performance criterion. Unlike the simple criteria that use only isolated characteristics of the dynamic response (e.g., decay ratio, settling time), the criteria of this category are based on the entire response of the process. The most often used are ... 

Another performance criterion that is also often used as a boundary condition for a separation problem is the purity of a product i, Pu . Product purity in a chromatographic process can be calculated by Equation 7.13, where Ncomp is the number of components in the solution ... 

Use of an interview to select employees is common place. As such hundreds of studies have been conducted on various aspects of the employment interview process. It is now well established that using a structured employment interview process improves criterion-related validity (Schmidt and Hunter 1998). Furthermore, a number of very good papers have been published on the process of developing a structured employment interview (e.g., Barclay 2001 Campion et al. 1997 HuflFcutt 2011 Levashina et al. 2014). The research evidence is clear that developing and using a highly stractured interview process can help provide a valid and reliable measure of a job applicant s ability to perform a job. However, like all selection measures, an interview is more suited to the measurement of some competencies than others. 

Green Chemistry, defined as the design of chemical products and processes that reduce or eliminate the use and generation of hazardous substances (5), has been referred to as pollution prevention at the molecular level. This emerging area recognizes fiiat during the design phase of any chemical synthesis, product, or process, minimized hazard must be viewed as a performance criterion. Moreover, hazard must also be viewed as a physical/chemical property that is possible to manipulate and control at die molecular level. 

One could be a horizontal cooperation between the operator and an expert tool whose ouq>uts are directly connected to the process. The principle is to make a dynamical repartition of the tasks using a task repartitor. The latter can be controlled either by the operator or by an optimal command algorithm, this according to a performance criterion of the man-expert tool-process system, and under the constraints imposed by the human limits. 

A simplification assumption, particularly for the formulation of the performance criterion, is that the process between the transitions is running at steady state, operating under perfect control, eliminating any disturbance and hence preventing any deviation from on-spec production. With this assumption, the production periods between the transition periods do not need to be considered in this study and the performance index accounts only for the transition periods. 

Schweickhardt and Allgower in Chapter A3 mainly concentrate on the nonlinearity assessment of processes. A comprehensive overview of general nonlinearity measures and a thorough investigation of the predictive and computational dimension of open loop measures are presented. As the main objective becomes the development of a tool to judge whether a nonlinear controller should be benefieial or needed for a particular process with specific nonlinear characteristics, the controller relevant nonlinearity is quantified. The selected measure is based on the relative differences between the output of nonlinear state feedback law and that of an equivalent linear state feedback law. The controller relevant nonlinearity measure depends not only on the plant dynamics and region of operation but also on the performance criterion used in the derivation of the controller law. 

The first two points are valid for open-loop process nonlinearity measures as well. The third point is new in control-relevant nonlinearity quantification. In a more general context, one has not only to consider the performance criterion but additionally mention the controller design method. Following the idea of Ref 24, optimal control theory with an integral performance criterion will be used here as it represents a benchmark for any achievable performance. Considering nonlinear internal model control with different filter time constants is also possible, see for example Ref 23. 

Repeatability is, perhaps, the most important performance criterion of a process-control valve. This is especially true in applications where precise flow or pressure control is needed for optimum performance of the process system. 

The reduced model should represent the original process as closely as possible. In order to compare the performance of the reduced model to the more complex model, one could define a quadratic performance criterion, such as ... 

Controller tuning can be defined as an optimisation process that involves a performance criterion related to the form of controller response and to the error between the process variable and the set point. When tuning a controller, some of the questions that may be asked include ... 

Model Predictive Control (MPC) has been also appUed to SC problems as a reactive approach. MPC is a control sfiategy based on the explicit use of a process model to predict the process performance over a period of time (Camacho and Bordons 1995). The model attempts to predict the control variables for a set of time periods. Predicted confiol variables depend on disturbance forecasts (i.e., demand, prices, and interest rates) and also on a set of given parameters that are known as control inputs. The MPC algorithm attempts to optimize a performance criterion that is a function of the control variables. Only a portion of the control variables, the portion corresponding to the following time period, is applied to the system. Next, as new control input information and disturbance forecasts are collected, the whole procedure is repeated, which produces a feed-forward effect and enables the system to counteract the environment dynamics. The procedure is illustrated in Fig. 1.6. 

Briefly, an MPC framework attempts to optimize a performance criterion that is a function of future control variables. By solving the optimization problem associated with the control algorithm all elements of the control signal are defined. However, only a portion of the control signal is applied to the system. Next, when new input control information, and disturbance forecasts, are collected, the whole procedure is repeated, producing a feed-forward effect and permitting the SC system to follow-up the process dynamics. 

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