Design of an intelligent system to control the technological system of ammonia production secondary condensation

Abstract

This paper has analyzed the functioning conditions for the technological system of secondary condensation (TSSC) in a typical ammonia synthesis unit of the AM-1360 series with the use of a system-control approach. The coordinates of control vectors and external disturbances have been determined. An algorithm has been developed for predicting the coordinates of the control vector for the subsystem of decision support under the conditions of external disturbances for such a complex inertial object with high metal consumption as TSSC. The method of mathematical modeling was used to determine, based on the developed algorithm, the patterns and quantitative dependences of the influence of external disturbances such as the temperature of primary condensation and the flow rate of circulation gas on the efficiency of TSSC heat exchange processes. The regularity of increase in the heat flows and coordinates of control vector with an increase in the temperature of primary condensation has been established. The parametric sensitivity of the coordinates of the control vector under the conditions of change in the temperature of the primary condensation has been determined, which, compared with the circulation gas flow rate, exceeds it by more than six times. The executed software implementation of the algorithm employing the MATLAB programming environment makes it possible, owing to the embedded client part (ORC client), free software access to the current data on the technological process. The functional structure of computer-integrated TSSC technology with the proposed correction subsystem under a supervisory control mode has been designed. Correction solutions involving the additional hardware and software based on the programmable logic controller VIPA and SCADA-system Zenon have been practically implemented. The implementation of the developed system ensures the stabilization of the secondary condensation temperature at the regulatory level of −5 °C, which reduces the consumption of natural gas by almost 1 million nm3 per year.

Developing a model of smart home usage among it specialists: the role of machine learning

“…SH is a new technology that aims to automate household appliances and every tool in the house for the convenience of users [12,13]. Knowledge in machine learning is required for effective management of the SH [14].…”