Developments in Fatty Acid-Derived Insect Pheromone Production Using Engineered Yeasts

The use of traditional chemical insecticides for pest control often leads to environmental pollution and a decrease in biodiversity. Recently, insect sex pheromones were applied for sustainable biocontrol of pests in fields, due to their limited adverse impacts on biodiversity and food safety compared to that of other conventional insecticides. However, the structures of insect pheromones are complex, and their chemical synthesis is not commercially feasible. As yeasts have been widely used for fatty acid-derived pheromone production in the past few years, using engineered yeasts may be promising and sustainable for the low-cost production of fatty acid-derived pheromones. The primary fatty acids produced by Saccharomyces cerevisiae and other yeasts are C16 and C18, and it is also possible to rewire/reprogram the metabolic flux for other fatty acids or fatty acid derivatives. This review summarizes the fatty acid biosynthetic pathway in S. cerevisiae and recent progress in yeast engineering in terms of metabolic engineering and synthetic biology strategies to produce insect pheromones. In the future, insect pheromones produced by yeasts might provide an eco-friendly pest control method in agricultural fields

Denoising and Trend Terms Elimination Algorithm of Accelerometer Signals

Acceleration-based displacement measurement approach is often used to measure the polish rod displacement in the oilfield pumping well. Random noises and trend terms of the accelerometer signals are the main factors that affect the measuring accuracy. In this paper, an efficient online learning algorithm is proposed to improve the measurement precision of polish rod displacement in the oilfield pumping well. To remove the random noises and eliminate the trend term of accelerometer signals, the ARIMA model and its parameters are firstly derived by using the obtained data of time series of acceleration sensor signals. Secondly, the period of the accelerometer signals is estimated through the Rife-Jane frequency estimation approach based on Fast Fourier Transform. With the obtained model and parameters, the random noises are removed by employing the Kalman filtering algorithm. The quadratic integration of the period is calculated to obtain the polish rod displacement. Moreover, the windowed recursive least squares algorithm is implemented to eliminate the trend terms. The simulation results demonstrate that the proposed online learning algorithm is able to remove the random noises and trend terms effectively and greatly improves the measurement accuracy of the displacement

Evolutionary algorithms for solving multi-modal and multi-objective optimization problems

In artificial intelligence, evolutionary algorithms (EAs) have shown to be effective and robust in solving difficult optimization problems. EAs are generic population-based metaheuristic optimization algorithms. The mechanisms used in EAs are inspired by biological evolution: reproduction, mutation, recombination, and selection. The development of EAs can be classified into two categories: single objective and multi-objective optimization. In this thesis, both single objective and multi-objective evolutionary algorithms have been studied. For single objective optimization, various niching techniques are integrated with differential evolution (DE) and particle swarm optimization (PSO) for multi-modal optimization. Multi-modal optimization deals with optimization tasks that involve finding all or most of the global/local peaks in one single run. EAs in their original forms are usually designed for locating one single global solution. To promote and maintain formation of multiple stable subpopulations within a single population, we introduced a neighborhood mutation technique to enhance DE with ability of handling multi-modal problems. We also proposed a locally informed PSO to tackle multi-modal optimization. Beside these, several existing niching techniques from the literature were modified and improved by us. For multi-objective evolutionary algorithms, we proposed a summation of normalized objective values and diversified selection (SNOV-DS) method to replace the classical non-domination sorting. The process of classical non-domination sorting is complex and time consuming. By use of the proposed method, not only the simulation speed is increased, but also the performance of the algorithm is improved. We also introduced an ensemble of constraint handling methods (ECHM) to solve constrained multi-objective optimization problems, where each constraint handling method had its own population. ECHM allows different constraint handling methods to generate offspring and exchange information. In this way, the offspring produced by the most suitable constraint handling method will survive and be set as parents for next generation. Lastly, we applied the proposed algorithm to solve environmental/economic power dispatch problem. We demonstrated the superior performance of the proposed algorithm over other similar evolutionary algorithms reported in literature.DOCTOR OF PHILOSOPHY (EEE

Research on Collaborative Optimal Dispatching of Electric Heating Integrated Energy Based on Wind Power Prediction Accuracy

By improving the accuracy of wind power prediction, reliable data support is provided for the Economic Dispatch(ED) model of Electric and Heat Integrated Energy System(EHIES) based on wind power consumption. In this paper, Convolutional Neural Network(CNN), Bidirectional Long Short Term Memory (BI-LSTM) and Attention Mechanism(AM) are used for wind power prediction. For the input of the CNN-BI-LSTM-AM model, the intrinsic mode signals obtained through Variational Mode Decomposition (VMD) and multidimensional power time series are used. Taking the October data of a coastal wind farm in Liaoning Province as an example, the accuracy of the CNN-BI-LSTM-AM model for wind power prediction reached 97.81%, which is better than the traditional model. Exploring the influence of wind power prediction accuracy on economy, wind power utilization efficiency, and carbon emissions in Two-stage robust optimization (TRO) and deterministic optimization models for EHIES. The EHIES model proposed in this paper considers the deterministic and uncertain economic dispatch of wind power under the situation of conventional heat load and heat load demand when the temperature drops suddenly. By solving the deterministic scheduling model and uncertain two-stage robust optimal scheduling model under different wind power forecasting accuracy, the electric heating integrated energy system can obtain the scheduling scheme with the lowest system operation cost, the highest wind power utilization and the less carbon emissions under the “universal” and “worst” scenarios, respectively. The simulation results show that compared with the traditional CPLEX solver, the C&CG algorithm can solve the main problem and sub problem alternately based on the column constraint generation algorithm and the strong duality theory. It can obtain the optimal solution of the economic dispatch model, providing an important reference for the optimal economic dispatch and wind power consumption strategy of the EHIES with wind power access

Denoising and Trend Terms Elimination Algorithm of Accelerometer Signals

Acceleration-based displacement measurement approach is often used to measure the polish rod displacement in the oilfield pumping well. Random noises and trend terms of the accelerometer signals are the main factors that affect the measuring accuracy. In this paper, an efficient online learning algorithm is proposed to improve the measurement precision of polish rod displacement in the oilfield pumping well. To remove the random noises and eliminate the trend term of accelerometer signals, the ARIMA model and its parameters are firstly derived by using the obtained data of time series of acceleration sensor signals. Secondly, the period of the accelerometer signals is estimated through the Rife-Jane frequency estimation approach based on Fast Fourier Transform. With the obtained model and parameters, the random noises are removed by employing the Kalman filtering algorithm. The quadratic integration of the period is calculated to obtain the polish rod displacement. Moreover, the windowed recursive least squares algorithm is implemented to eliminate the trend terms. The simulation results demonstrate that the proposed online learning algorithm is able to remove the random noises and trend terms effectively and greatly improves the measurement accuracy of the displacement

Comparative study of reformed neural network based short-term wind power forecasting models

Short-term prediction of wind power plays a vital role in wind power application. In order to improve the accuracy of wind power forecasting, this paper investigates neural network combined forecasting models to forecast the wind power, the data of a real wind farm, the Pacific Wind Farm, is used. In view of the difficulty of predicting the large fluctuations of wind power, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm is used to decompose the wind power time series which can reduce the complexity of the forecasting process, and then the intrinsic mode function (IMF) signal is predicted by the BP Neural Network, wavelet neural network (WNN) and long short-term memory (LSTM) neural network respectively, and the final result is obtained through wavelet reconstruction. By comparing with a single model, the combined prediction model has better prediction accuracy and stability, among them, the NMAE predicted by CEEMDAN-GA-BP in January was 4.167%, and the NRMSE was 6.590%. Reformed neural network based short-term wind power forecasting models proposed in here provides very useful information for operation and control of high renewable energy penetrated power systems.</p

Fundamental Properties of Nonlinear Stochastic Differential Equations

The existence of solutions is used the premise of discussing other properties of dynamic systems. The goal of this paper is to investigate the fundamental properties of nonlinear stochastic differential equations via the Khasminskii test, including the local existence and global existence of the solutions. Firstly, a fundamental result is given as a lemma to verify the local existence of solutions to the considered equation. Then, the equivalent proposition for the global existence and the fundamental principle for the Khasminskii test are formally established. Moreover, the classical Khasminskii test is generalized to the cases with high-order estimates and heavy nonlinearity for the stochastic derivatives of the Lyapunov functions. The role of the noise in this aspect is especially investigated, some concrete criteria are obtained, and an application for the role of the noise in the persistence of financial systems is accordingly provided. As another application of the fundamental principle, a new version of the Khasminskii test is established for the delayed stochastic systems. Finally the conclusions obtained in the paper are verified by simulation. The results show that, under weaker conditions, the global existence of better solutions to stochastic systems to those in the existing literature can be obtained

Economic and low-carbon island operation scheduling strategy for microgrid with renewable energy

With the advancement of supply-side reforms in the energy and power fields, a comprehensive energy system that integrates various energy sources such as electricity, heat, and natural gas has become the main development trend. Based on this, this paper proposes a combined heat and power(CHP) microgrid model with renewable energy. Based on the improved particle swarm algorithm, the optimal scheduling problem of the microgrid model is calculated, and a feasible optimal scheduling strategy considering both economy and low carbon is proposed. Finally, the economy and correctness of the optimal scheduling strategy proposed in this paper are verified by simulation