Search

Objective: This study aimed to explore the effects of group prenatal health care combined with happiness training on delivery mode and maternal role adaptation in elderly primiparous women. Methods: A total of 110 elderly primiparous women who were expected to deliver in hospital from January 2020 to December 2021 were selected and assigned to two equal size groups: Group A and Group B. Results:After the nursing intervention, the natural delivery rate of Group A was 85.45%, significantly higher than that of Group B at 52.73% (PConclusion: Group prenatal health care combined with happiness training can improve the delivery mode of elderly primiparous women, help them adapt better to their maternal role, and enhance their subjective sense of well-being.

The ultra-supercritical power plant is an advanced energy conversion system with high coal utilization and low emission. It is a complex system associated with high nonlinearity, strong coupling, and subjected to various uncertainties and interferences. This article introduces a nonlinear generalized predictive control based on a composite weighted human learning optimization network to incorporate both local linear modeling and local control into a multi-layer dynamic structure. Since composite weighted human learning optimization network can be considered as a linear-in-weight network, the limitation of the non-convex is elaborately broken through incorporating composite weighted human learning optimization models into the convex quadratic programming routine of local linear generalized predictive control. Detailed analysis is given to show the validity and advantage of the proposed method in controlling the ultra-supercritical power plant.

A generalized predictive control method based on neuro-fuzzy network (NFN-GPC) is presented for a 1000-MW ultra-supercritical (USC) power plant to improve control performance. First, to decrease the nonlinearity, local linear models are elaborately constructed for approximating the studied system by virtue of neuro-fuzzy network (NFN). Second, a compensation mechanism is delicately developed to further increase the accuracy of local models. Through gauss function and B-spline function, the memberships of local models which represent the weights of local regions are determined. Finally, based on the obtained models, a multi-variable generalized predictive controller is designed to realize the optimal control over the whole operating region combined with the membership of the current neuro-fuzzy network. This scheme closely connects engineering with artificial intelligence; compared with traditional generalized predictive control, the merit of proposed NFN-GPC is that it can capture the details over the whole operating range which can get more accurate and faster control effects. The simulation results show that the proposed neuro-fuzzy generalized predictive control method can achieve the satisfactory performance even in the case of strong coupling and nonlinearity. In conclusion, the proposed method is an effective long-term approach to control the USC power plant.

This article proposes a fuzzy k-means cluster based generalized predictive control (GPC) method for a 1000 MW ultra supercritical power plant to improve of the boiler combustion efficiency. First, to fully use the statistic characteristic of the historical data, a fuzzy k-mean cluster network (FKN) is well constructed to derive the local linear models, and the nonlinear dynamic process of studied system is elaborately approximated by the fuzzy combination of the local linear models. Then, a global GPC method is proposed to improve the control performance by using the membership of the current FKN. Different from the traditional GPC, the advantage of proposed GPC is that local GPC is fuzzily combined together to achieve the purpose of global GPC by a scheduling algorithm. Finally, an example illustrates that the proposed control strategy can achieve the satisfactory performance.

Main steam temperature is an important parameter in thermal power plants. In order to get a better control effect, an improved fuzzy genetic algorithm (FGA) modelling method is proposed in this paper. Different from traditional identification methods, the improved FGA is more suitable for modeling the dynamic process of the main steam temperature which has the characteristics of strong coupling and nonlinearity. Firstly, to solve the nonlinear problem, a fuzzy thought is introduced to partition the whole operating region into several local regions. Then an improved genetic algorithm (GA) which has better identification capability is proposed to build the linear model in each local region. The nonlinear dynamic process of studied system is elaborately approximated by the fuzzy combination of the local linear models. The simulation results show that the improved FGA can achieve satisfactory effects even in the case of strong coupling and nonlinearity, which provides theory foundation for other control methods.

Reliable control of the main steam temperature is a necessary condition to achieve the safe and economic performance of the thermal power plants. For the purpose of improving accuracy of control system, an improved fuzzy particle swarm optimization (FPSO) algorithm is proposed for the modeling of the main steam temperature in the ultra-supercritical (USC) units in this paper. Different from the traditional methods, the nonlinear problem is effectively solved by a fuzzy k-means network which contains local models and fuzzy rules. In the network, an improved particle swarm optimization (PSO) that has faster convergence speed is introduced to build the local models. And the nonlinear dynamic process of the main steam temperature is elaborately approximated by the fuzzy fusion of the local linear models. Simulation results show that the improved FPSO algorithm proposed in this paper can achieve better performance with higher convergence speed and identification accuracy, which is conducive to the subsequent control of boiler combustion process.