Process control, chemical sensors

Process control. Chemical engineers will develop and implement better sensors for temperature, pressure, and chemical composition. Processes will be designed to integrate artificial intelligence for process control, monitoring, and safety. 

Electrochemical Microsensors. The most successful chemical microsensor in use as of the mid-1990s is the oxygen sensor found in the exhaust system of almost all modem automobiles (see Exhaust control, automotive). It is an electrochemical sensor that uses a soHd electrolyte, often doped Zr02, as an oxygen ion conductor. The sensor exemplifies many of the properties considered desirable for all chemical microsensors. It works in a process-control situation and has very fast (- 100 ms) response time for feedback control. It is relatively inexpensive because it is designed specifically for one task and is mass-produced. It is relatively immune to other chemical species found in exhaust that could act as interferants. It performs in a very hostile environment and is reHable over a long period of time (36). 

Die Fabrik auf dem Chip, Spektrum der Wissenschafi, October 2002 Miniaturization and modularization of parts of future chemical apparatus general advantages of micro flow expert opinions specialty and fine chemical applications leading position of German technology flexible manufacture large-capacity micro reactors reformers for small-capacity applications compatible and automated micro-reaction systems process-control systems temperature and pressure sensors [209]. 

Small but environrrientallyjnendly. The Chemical Engineer, March 1993 Huge increases in technology in the past distributed manufacturing in small-scale plants miniaturization of processes domestic methanol plant point-of-sale chlorine simpler and cheaper plants economy of plant manufacture process control and automation start-up and shut-down sensor demand [145],... 

A more complicated situation in process regulation occurs when among various chemical properties there is not one that can explicitly be indicated as a key test, so that more than one sensor (1,2,..., n) has to be used, in which case one refers to "multivariable systems in process control 6. 

Many physio-chemical processes involve a time delay between the input and output. This delay may be due to the time required for a slow chemical sensor to respond, or for a fluid to travel down a pipe. A time delay is also called dead time or transport lag. In controller design, the output will not contain the most current information, and systems with dead time can be difficult to control. 

The strategy depends on the situation and how we measure the concentration. If we can rely on pH or absorbance (UV, visible, or Infrared spectrometer), the sensor response time can be reasonably fast, and we can make our decision based on the actual process dynamics. Most likely we would be thinking along the lines of PI or PID controllers. If we can only use gas chromatography (GC) or other slow analytical methods to measure concentration, we must consider discrete data sampling control. Indeed, prevalent time delay makes chemical process control unique and, in a sense, more difficult than many mechanical or electrical systems. 

One of the major breakthroughs in nanotechnology is the use of nanomaterials as catalysts for environmental applications [149]. Nanomaterials have been developed to improve the properties of catalysts, enhance reactivity towards pollutants, and improve their mobility in various environmental media [150]. Nanomaterials offer applications to pollution prevention through improved catalytic processes that reduce the use of toxic chemicals and eliminate wastes. Nanomaterials also offer applications in environmental remediation and, in the near future, opportunities to create better sensors for process controls. 

Miller H.H., Hirschfeld T.B., Fiber optic chemical sensors for industrial and process control, Proc. SPIE-Int. Soc. Opt. Eng. 1987 718 39. 

The second main reason for wastewater quality monitoring is related to process control, particularly for treatment plants where analysers and sensors are generally used with physico-chemical or biological reactors, including settling tanks. This application is mainly encountered for important wastewater treatment plants, either urban (majority domestic) or industrial, where the storage capacities are rather small with regard to the flow to be treated. Obviously, on-line systems are preferable in this case, but the available instruments often limit the choice. 

In parallel with improvements in chemical sensor performance, analytical science has also seen tremendous advances in the development of compact, portable analytical instruments. For example, lab-on-a-chip (LOAC) devices enable complex bench processes (sampling, reagent addition, temperature control, analysis of reaction products) to be incorporated into a compact, device format that can provide reliable analytical information within a controlled internal environment. LOAC devices typically incorporate pumps, valves, micromachined flow manifolds, reagents, sampling system, electronics and data processing, and communications. Clearly, they are much more complex than the simple chemo-sensor described above. In fact, chemosensors can be incorporated into LOAC devices as a selective sensor, which enables the sensor to be contained within the protective internal environment. Figure 5... 

Production of the API begins with the selection of a synthetic route, as determined in the development program. Raw materials are added into a reaction vessel. These raw materials as reactants are heated or cooled in the reaction vessel (normal range is from -15 to 140 °C purpose-built vessels are needed for extreme reactions that require lower or higher temperature controls or pressurization of reaction processes). The chemical synthesis reactions are monitored and controlled via sensor probes (pH, temperature, and pressure) with in-process feedback controls for adjustments and alarms when necessary. Samples are withdrawn at dehned intervals for analysis to determine the reaction progress. Catalysts, including enzymes, may be added to speed up and direct the reaction along a certain pathway. 

NN applications, perhaps more important, is process control. Processes that are poorly understood or ill defined can hardly be simulated by empirical methods. The problem of particular importance for this review is the use of NN in chemical engineering to model nonlinear steady-state solvent extraction processes in extraction columns [112] or in batteries of counter-current mixer-settlers [113]. It has been shown on the example of zirconium/ hafnium separation that the knowledge acquired by the network in the learning process may be used for accurate prediction of the response of dependent process variables to a change of the independent variables in the extraction plant. If implemented in the real process, the NN would alert the operator to deviations from the nominal values and would predict the expected value if no corrective action was taken. As a processing time of a trained NN is short, less than a second, the NN can be used as a real-time sensor [113]. 

The growing nse of more complex PAT (versus the historically used simple univariate sensors such as pressure, temperature, pH, etc.) within manufacturing industries is driven by the increased capabilities of these systems to provide scientihc and engineering controls. Increasingly complex chemical and physical analyses can be performed in, on, or immediately at, the process stream. Drivers to implement process analytics include the opportunity for live feedback and process control, cycle time reduction, laboratory test replacement as well as safety mitigation. All of these drivers can potentially have a very inunediate impact on the economic bottom line, since product quality and yield may be increased and labor cost reduced. 

The application of this concept to liquid samples is what we already refer to electronic tongue . It entails the use of multidimensional information coming from an array of chemical sensors, mimicking the animal sense of taste. As several possibilities exist on the side of which sensors form the array, the general response shown by the different sensors used is of paramount importance that is, cross-selectivity features are needed in order to profit from the multidimensional aspects of the information [7]. The performance of electronic tongues can be suited not only to qualitative purposes like identification of species and classification of sample varieties, but also to quantitative uses, normally the multidetermination of a set of chemical species, an interesting objective for process control. A more bioinspired trend is the artificial taste [8] in order to perform automated taste perception, especially in the industrial field. 

---