Design of Hybrid Model of Fertilizers Production Process for Automatic Control Purpose

摘要

Fertilizers are broadly divided into organic fertilizers (composed of enriched organic matter - plant or animal), or inorganic fertilizers (composed of synthetic chemicals and/or minerals). Inorganic fertilizer is often synthesized using the Haber-Bosch process, which produces ammonia as the end product. This ammonia is used as a feedstock for other nitrogen fertilizers, such as anhydrous ammonium nitrate and urea. Control technologies develope very rapidly, and process automatic control is now ubiquitous within industries. In receant years, modern automatic control technologies are closely related to modelling in chemical industries. For the high quality automatic control purpose one needs to have a reliable process model. Besides, chemical industries save money using models for process control and optimization purposes [1]. With increasing reliance on information technology, systematic decision-making strategies are essential for effective and efficient performance including fertilizers industry systems. To maintain its successes, the industry must be flexible and adaptable to new technologies, external pressures and changing markets. All of these challenges require systematic improvement methodology and advanced technologies for control, optimization and planning. The most commonly used advanced control techniques include such efficiency inferential without analyzer based optimization and modelling, process modeling and system identification (first-principle models and empirical models). The chemical processes under consideration is characterized by the complex reaction systems [2, 4, and 6]. Hence, it is not possible to describe the process in detail using only first-principle and mechanistic models. To overcome the limitations the hybrid modelling has been used in chemical and biochemical engineering for many years (e. g., [6]). Besides, often it is difficult to describe the chemical processes using only classic methods, which are given in the automatic control theory. Although, using hybrid models improve benefits for modelling and identification of chemical or biochemical processes. On the other hand, the successful identification of hybrid models using "black box" models is possible having consistent experimental data. One needs to use suitable hybrid model identification method, which gives good modelling quality and is effective and robust [6]. Besides, hybrid model application for chemical processes requires considering the application of artificial neuron networks. Moreover, in chemical technology one is most often interested in modelling of specific reaction rates, because they define the behaviour of the conversions in chemical processes. Specific reaction rates have to be determined using feed-forward artificial neural networks where the inputs are the key factors influencing the process dynamics - the concentrations of the reacting components, temperature, pressure, etc. In the recently published literature, some interesting examples show that hybrid combination of artificial neural networks, mechanistic kinetics and mass balance equations can lead to considerable advantages [2, 6]. On the other hand, many classical identification methods [3] may fail or do not lead to a solution of required precision.