On-line Estimation of Biomass in Fermentation Process Using Support Vector Machine

Biomass is a key factor in fermentation process, directly influencing the performance of the fermentation system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine （SVM） for an on-line biomass estimation was analyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.

On-line Estimation in Fed-batch Fermentation Process Using State Space Model and Unscented Kalman Filter

On-line estimation of unmeasurable biological variables is important in fermentation processes,directly influencing the optimal control performance of the fermentation system as well as the quality and yield of the targeted product.In this study,a novel strategy for state estimation of fed-batch fermentation process is proposed.By combining a simple and reliable mechanistic dynamic model with the sample-based regressive measurement model,a state space model is developed.An improved algorithm,swarm energy conservation particle swarm optimization(SECPSO) ,is presented for the parameter identification in the mechanistic model,and the support vector machines(SVM) method is adopted to establish the nonlinear measurement model.The unscented Kalman filter(UKF) is designed for the state space model to reduce the disturbances of the noises in the fermentation process.The proposed on-line estimation method is demonstrated by the simulation experiments of a penicillin fed-batch fermentation process.

On-line Estimation of Biomass in Fermentation Process Using Support Vector Machine

Biomass is a key factor in fermentation process, directly influencing the performance of the fermenta- tion system as well as the quality and yield of the targeted product. Therefore, the on-line estimation of biomass is indispensable. The soft-sensor based on support vector machine (SVM) for an on-line biomass estimation was ana- lyzed in detail, and the improved SVM called the weighted least squares support vector machine was presented to follow the dynamic feature of fermentation process. The model based on the modified SVM was developed and demonstrated using simulation experiments.

On-Line Structural Breaks Estimation for Non-stationary Time Series Models

Non-stationary time series could be divided into piecewise stationary stochastic signal. However, the number and locations of breakpoints, as well as the approximation function of the respective segment signal are unknown. To solve this problem, a novel on-line structural breaks estimation algorithm based on piecewise autoregressive processes is proposed. In order to find the "best" combination of the number, lengths, and orders of the piecewise autoregressive (AR) processes, the Akaikes Information Criterion (AIC) and Yule-Walker equations are applied to estimate an AR model fit to the data. Numerical results demonstrate that the proposed estimation algorithm is suitable for different data series. Furthermore, the algorithm is used in a clinical study of electroencephalogram (EEG) with satisfactory results, and the ability to deal with real-time data is the most outstanding characteristic of on-line structural breaks estimation algorithm proposed.

Estimation of Digestible Energy Values of Plant Protein Supplement in Pig

[Objective] The aim of the study was to establish the effective and accurate formulas for estimating the digestible energy （DE） values of plant protein supplement in pig. [Method] By difference method with different amount of alternative feeds （20% -50%）, two4 x4 Latin- square-designed trials were taken on eight castrated male pigs [ Yorkshire x Landrace x Neijiang pig, initial body-weight： （46 ±2） kg ] to deter- mine the apparent digestible energy （ADE） of the eight kinds of plant protein supplement commonly used in China, that is, corn gluten meal （sol.）, soybean meal （ sol. ）, fababean, pea, rapeseed meal （ sol. ）, sesame meal （ sol. ）, rapeseed meal （ exp. ） and cotton seed meal （sol.）. [Resultl （1） Fiber was the most important factor to estimate the ADE of plant protein supplement in pigs, and ADF was the best one. （2） The most effective equations were as below： （ 1 ） OE （kJ/kg DM） = 14 741.86 - 185.01ADF＋54.01SCHO＋22.45CP （ R =0.988,RSD= 67.9,P〈0.01 ） ; （2） DE （kJ/kg DM） =22 223.26 -209.58ADF＋26.79SCHO-1.09GE （ Ff =0.989,RSD=66.9, P〈0.01 ） . [Conclusion] The accurate, practical and specific regression equations were established for DE prediction of plant protein supplement in pig.

Stochastic Programming for Hub Energy Management Considering Uncertainty Using Two-Point Estimate Method and Optimization Algorithm

To maximize energy profit with the participation of electricity,natural gas,and district heating networks in the day-ahead market,stochastic scheduling of energy hubs taking into account the uncertainty of photovoltaic and wind resources,has been carried out.This has been done using a new meta-heuristic algorithm,improved artificial rabbits optimization(IARO).In this study,the uncertainty of solar and wind energy sources is modeled using Hang’s two-point estimating method(TPEM).The IARO algorithm is applied to calculate the best capacity of hub energy equipment,such as solar and wind renewable energy sources,combined heat and power(CHP)systems,steamboilers,energy storage,and electric cars in the day-aheadmarket.The standard ARO algorithmis developed to mimic the foraging behavior of rabbits,and in this work,the algorithm’s effectiveness in avoiding premature convergence is improved by using the dystudynamic inertia weight technique.The proposed IARO-based scheduling framework’s performance is evaluated against that of traditional ARO,particle swarm optimization(PSO),and salp swarm algorithm(SSA).The findings show that,in comparison to previous approaches,the suggested meta-heuristic scheduling framework based on the IARO has increased energy profit in day-ahead electricity,gas,and heating markets by satisfying the operational and energy hub limitations.Additionally,the results show that TPEM approach dependability consideration decreased hub energy’s profit by 8.995%as compared to deterministic planning.

Estimation of Weibull Distribution Parameters for Wind Speed Characteristics Using Neural Network Algorithm

Harvesting the power coming from the wind provides a green andenvironmentally friendly approach to producing electricity. To facilitate theongoing advancement in wind energy applications, deep knowledge aboutwind regime behavior is essential. Wind speed is typically characterized bya statistical distribution, and the two-parameters Weibull distribution hasshown its ability to represent wind speeds worldwide. Estimation of Weibullparameters, namely scale (c) and shape (k) parameters, is vital to describethe observed wind speeds data accurately. Yet, it is still a challenging task.Several numerical estimation approaches have been used by researchers toobtain c and k. However, utilizing such methods to characterize wind speedsmay lead to unsatisfactory accuracy. Therefore, this study aims to investigatethe performance of the metaheuristic optimization algorithm, Neural NetworkAlgorithm (NNA), in obtaining Weibull parameters and comparing itsperformance with five numerical estimation approaches. In carrying out thestudy, the wind characteristics of three sites in Saudi Arabia, namely HaferAl Batin, Riyadh, and Sharurah, are analyzed. Results exhibit that NNA hashigh accuracy fitting results compared to the numerical estimation methods.The NNA demonstrates its efficiency in optimizing Weibull parameters at allthe considered sites with correlations exceeding 98.54.

Energy efficiency enhancement of two-way amplifier forward relaying with channel estimation error

In order to save energy consumption of two-way amplifier forward(AF) relaying with channel estimation error, an energy efficiency enhancement scheme is proposed in this work. Firstly, through the analysis of two-way AF relaying mode with channel estimation error, the resultant instantaneous SNRs at end nodes is obtained. Then, by using a high SNR approximation, outage possibility is acquired and its simple closed-form expression is represented. Specially, for using the energy resource more efficiently, a low-complexity power allocation and transmission mode selection policy is proposed to enhance the energy efficiency of two-way AF relay system. Finally, relay priority region is identified in which cooperative diversity energy gain can be achieved. The computer simulations are presented to verify our analytical results, indicating that the proposed policy outperforms direct transmission by an energy gain of 3 dB at the relative channel estimation error less than 0.001. The results also show that the two-way AF relaying transmission loses the two-way AF relaying transmission loses its superiority to direct transmission in terms of energy efficiency when channel estimation error reaches 0.03.

Energy flow problem solution based on state estimation approaches and smart meter data

Accurate electric energy(EE)measurements and billing estimations in a power system necessitate the development of an energy flow distribution model.This paper summarizes the results of investigations on a new problem related to the determination of EE flow in a power system over time intervals ranging from minutes to years.The problem is referred to as the energy flow problem(EFP).Generally,the grid state and topology may fluctuate over time.An attempt to use instantaneous(not integral)power values obtained from telemetry to solve classical electrical engineering equations leads to significant modeling errors,particularly with topology changes.A promoted EFP model may be suitable in the presence of such topological and state changes.Herein,EE flows are determined using state estimation approaches based on direct EE measurement data in Watt-hours(Volt-ampere reactive-hours)provided by electricity meters.The EFP solution is essential for a broad set of applications,including meter data validation,zero unbalance EE billing,and nontechnical EE loss check.

Facing a Problem of Electrical Energy Quality in Ship Networks-measurements, Estimation, Control

In this paper, electrical energy quality and its indices in ship electric networks are introduced, especially the meaning of electrical energy quality terms in voltage and active and reactive power distribution indices. Then methods of measurement of marine electrical energy indices are introduced in details and a microprocessor measurement-diagnosis system with the function of measurement and control is designed. Afterwards, estimation and control of electrical power quality of marine electrical power networks are introduced. And finally, according to the existing method of measurement and control of electrical power quality in ship power networks, the improvement of relative method is proposed.

Solar Energy Generation Potential Estimation in India and Gujarat, Andhra, Telangana States

It is well known that the rampant increase for the demand of electricity and rapid depletion of the fossil fuels has called for immediate response in the direction of energy sufficiency. To accomplish this, one of the important tasks is to identify the locations of high potential for renewable energy generation. It is a well-established fact that solar energy proved to be the most sought after source for energy generation. Although, solar energy potential maps of India have been prepared based on solar irradiation maps in the earlier studies, the present research study has been carried out with a focused attention directly on solar energy generation considering various parameters. In this work it is shown that solar energy generation does not depend on solar radiation alone at a location. Instead, there are various other factors that influence the energy generation. Some of them are ambient temperature, wind velocity and other parameters like weather and topographic conditions. In this study the locations with high and low solar energy generation potential in India have been identified through systematic analysis by computing the solar energy parameters at every grid point (1°× 1°). The work has been extended with more detailed study for Gujarat, Andhra Pradesh and the newly formed Telangana states. The data points considered for the states are 0.25°× 0.25°having resulted in adding more number of locations. Our results indicate that the total annual energy generation in India varies from 510,000 KWH to 800,000 KWH per acre of land. The least energy generation location pertains to the eastern parts of Arunachal Pradesh and eastern part of Assam and the highest annual solar energy generation has been identified in the eastern parts of Jammu & Kashmir and eastern part of Uttarakhand.

ESTIMATION OF THEORETICAL POTENTIAL AND EXPLOITABLE QUANTITY OF CHINA'S OCEANIC THERMAL ENERGY

Two estimaton methods are used to calculate the theoretical reservoir potential of China's oceanic thermal energy. One is based on the measured temperature difference between the surface water and the deep water, the other on the net radiation energy income from solar insolation either measured or deduced. The results from these two methods are compared and examined. Then, the maximum amount of the exploitable thermal energy is calculated based on the assumption of a Carnot cycle efficiency. In the process of estimation, such factors as water depth, seasonal water temperature variation and geographic location have been taken into account.The theoretical reservoir capacity and the exploitable quantity of the thermal energy of China's four seas are thus estimated separately.

Scalable Distributed State Estimation over Binary Sensor Networks with Energy Harvester

This paper deals with distributed state estimation problem for discrete time-varying systems over binary sensor networks,where every binary sensor is equipped with an energy harvester.The input of every binary sensor considers the randomly occurring missing measurements.The differences between the real and estimated inputs of binary sensor are employed to derive useful information in order to address the insufficient information for estimation purpose.The information from neighboring nodes is transmitted only if its energy level is positive,where a random variable is introduced to formulate the energy level.By means of the available information,distributed estimator is constructed for each binary sensor and the desirable performance constraints is given for the dynamic characteristics of estimation errors within anite time horizon.Sucient conditions are established for the existence of desired distribution estimation quantities through local performance analysis methods.Also,the desired distributed estimator gains are calculated recursively,which means the desirable scalability.Ultimately,the viability and efficiency of the distributed scheme are exhibited through a practical illustration.

G-MER： Green Mobility Estimation- Based Routing Protocol in Wireless Sensor Network

As a core technology of Intemet of Things （loT）, Wireless Sensor Network （WSN） has become a research hotspot recently. More and more WSNs are being deployed in highly mobile environments. The fast moving sensor nodes bring significant challenges for the routing decision. In this paper, we propose an efficient logical location method, and designe a mobility estimating metric and derive a novel Green Mobility Estirmtion- based Routing protocol （G-MER） for WSNs. We also set up a full framework to evaluate its per- formance. Simulation results illustrate that G-MER achieves a fairly better perforrmnce in terrm of broadcast times and link failures than AODV. What＇s more, it decreases the mean hops by about 0.25 and reduces energy consumption by about 10% during the whole experiment. All the results show that G-MER can be effectively used in fast- moving and limited resource scenarios.

ESTIMATION OF DOPPLER CENTROID FREQUENCY IN SPACEBORNE SCANSAR

Doppler centroid frequency is an essential parameter in the imaging processing of the Scanning mode Synthetic Aperture Radar (ScanSAR). Inaccurate Doppler centroid frequency will result in ghost images in imaging result. In this letter, the principle and algorithms of Doppler centroid frequency estimation are introduced. Then the echo data of ScanSAR system is analyzed. Based on the algorithms of energy balancing and correlation Doppler estimator in the estimation of Doppler centroid frequency in strip mode SAR, an improved method for Doppler centroid frequency estimation in ScanSAR is proposed. The method has improved the accuracy of Doppler centroid frequency estimation in ScanSAR by zero padding between burst data. Finally, the proposed method is validated with the processing of ENVIronment SATellite Advanced Synthetic Aperture Radar (ENVISAT ASAR) wide swath raw data.

State Estimation of Regional Power Systems with Source-Load Two-Terminal Uncertainties

The development and utilization of large-scale distributed power generation and the increase of impact loads represented by electric locomotives and new energy electric vehicles have brought great challenges to the stable operation of the regional power grid.To improve the prediction accuracy of power systems with source-load twoterminal uncertainties,an adaptive cubature Kalman filter algorithm based on improved initial noise covariance matrix Q0 is proposed in this paper.In the algorithm,the Q0 is used to offset the modeling error,and solves the problem of large voltage amplitude and phase fluctuation of the source-load two-terminal uncertain systems.Verification of the proposed method is implemented on the IEEE 30 node system through simulation.The results show that,compared with the traditional methods,the improved adaptive cubature Kalman filter has higher prediction accuracy,which verifies the effectiveness and accuracy of the proposed method in state estimation of the new energy power system with source-load two-terminal uncertainties.

Estimated Binding Energies of Drug-Like and Nondrug-Like Molecules in the Active Site of HIV-1 Integrase, 1BIS.pdb, and Two Mutant Models: Y143R and N155H

Lipinski’s “Rule of Five” was introduced for predicting oral bioavailability to describe drug-like molecules. For the purpose of this research the rules were used to separate potential inhibitors of HIV-1 integrase (1BIS.pdb) into two groups: drug-like and nondrug-like. If one of Lipinski’s “Rule of Five” was not followed the potential inhibitor was classified as nondrug-like. Thirty molecules were identified from the literature, twenty-four drug-like and six nondrug-like, that were docked into the active site of 1BIS.pdb (considered the non-mutated protein) and two mutant models, Y143R and N155H. These are two of the mutations that have led to increased resistance to HIV-1 integrase drugs such as raltegravir and elvitegravir. The computational software, ICM-Pro (Molsoft L.L.C.), was used to determine the estimated binding energy (EBE) of the drug/protein complex. It was found that the nondrug-like molecules generally had a more negative EBE, that is, tighter binding with 1BIS. pdb, though there were several exceptions in the drug-like group. With the protein mutant model Y143R, the majority of drug-like (58%) and nondrug-like molecules (67%) had tighter binding. However, for the mutant model N155H, there was the same percent (46%) of drug-like molecules with tighter binding with the mutant model as with 1BIS.pdb. The drug-like molecules were used when there was a ≥1 kcal/mole difference between 1BIS.pdb and either of the two mutant models to suggest a pharmacophore with structural characteristics for an HIV-1 integrase inhibitor.

Fluctuation-Model-Based Discrete Probability Estimation for Small Samples

A robust method is proposed for estimating discrete probability functions for small samples. The proposed approach introduces and minimizes a parameterized objective function that is analogous to free energy functions in statistical physics. A key feature of the method is a model of the parameter that controls the trade-off between likelihood and robustness in response to the degree of fluctuation. The method thus does not require the value of the parameter to be manually selected. It is proved that the estimator approaches the maximum likelihood estimator at the asymptotic limit. The effectiveness of the method in terms of robustness is demonstrated by experimental studies on point estimation for probability distributions with various entropies.

Data-driven source-load robust optimal scheduling of integrated energy production unit including hydrogen energy coupling

A robust low-carbon economic optimal scheduling method that considers source-load uncertainty and hydrogen energy utilization is developed.The proposed method overcomes the challenge of source-load random fluctuations in integrated energy systems(IESs)in the operation scheduling problem of integrated energy production units(IEPUs).First,to solve the problem of inaccurate prediction of renewable energy output,an improved robust kernel density estimation method is proposed to construct a data-driven uncertainty output set of renewable energy sources statistically and build a typical scenario of load uncertainty using stochastic scenario reduction.Subsequently,to resolve the problem of insufficient utilization of hydrogen energy in existing IEPUs,a robust low-carbon economic optimal scheduling model of the source-load interaction of an IES with a hydrogen energy system is established.The system considers the further utilization of energy using hydrogen energy coupling equipment(such as hydrogen storage devices and fuel cells)and the comprehensive demand response of load-side schedulable resources.The simulation results show that the proposed robust stochastic optimization model driven by data can effectively reduce carbon dioxide emissions,improve the source-load interaction of the IES,realize the efficient use of hydrogen energy,and improve system robustness.

Cost Estimate and Input Energy of Floor Systems in Low Seismic Regions

Reinforced concrete(RC)as a material is most commonly used for buildings construction.Several floor systems are available following the structural and architectural requirements.The current research study provides cost and input energy comparisons of RC office buildings of different floor systems.Conventional solid,ribbed,flat plate and flat slab systems are considered in the study.Building models in three-dimensional using extended threedimensional analysis of building systems(ETABS)and in two-dimensional using slab analysis by the finite element(SAFE)are developed for analysis purposes.Analysis and design using both software packages and manual calculations are employed to obtain the optimum sections and reinforcements to fit cities of low seismic intensities for all the considered building systems.Two ground motion records of low peak ground acceleration(PGA)levels are used to excite the models to measure the input energies.Uniformat cost estimating system is adopted to categorize building components according to 12 divisions.Also,Microsoft(MS)Project is utilized to identify the construction cost and duration of each building system.The study shows that floor system significantly causes changes in the input energy to structures.In addition,the slight increase in the PGA increases the amount of input energy particularly flat plate system.Estimated cost of the flat plate system that the flat slab system is of higher value as compared to ribbed and conventional slab systems.The use of drop panels increases this value as well.Moreover,the estimated cost of the ribbed slab system exceeds that of conventional system.