Optimization of heterogeneous Catalyst-assisted fatty acid methyl esters biodiesel production from Soybean oil with different Machine learning methods

There is a growing attention to the bio and renewable energies due to fast depletion of fossil fuels as well as the global warming problem. Here, we developed a modeling and simulation method by means of artificial intelligence (AI) for prediction of the bioenergy production from vegetable bean oil. AI methods are well known for prediction of complex and nonlinear process. Three distinct Adaptive Boosted models including Huber regression, LASSO, and Support Vector Regression (SVR) as well as artificial neural network (ANN) were applied in this study to predict actual yield of Fatty acid methyl esters (FAME) production. All boosted utilizing the Adaptive boosting algorithm. The important influencing parameters on the biodiesel production such as the catalyst loading (CAO/Ag, wt%) and methanol to oil (Soybean oil) molar ratio were selected as the input variables of models while the yield of FAME production was selected as output. Model hyper-parameters were tuned to maintain generality while improving prediction accuracy. The models were evaluated using three distinct metrics Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R². Error rates of 8.16780E-01, 4.43895E-01, 2.06692E + 00, and 3.92713 E-01 were obtained with the MAE metric for boosted Huber, SVR, LASSO and ANN models. On the other hand, the RMSE error of these models were about 1.092E-02, 1.015E-02, 2.669E-02, and 1.01174E-02, respectively. Finally, the R-square score were calculated for boosted Huber, boosted SVR, and boosted LASSO as 0.976, 0.990, 0.872, and 0.99702, respectively. Therefore, it can be concluded that although the boosted SVR and ANN models were better models for prediction of process efficiency in terms of error, but all algorithms had high accuracy. The optimum yield of 83.77% and 81.60% for biodiesel production were observed at optimum operating values from boosted SVR and ANN models, respectively.     

Diffusive search with spatially dependent resetting

We consider a stochastic search model with resetting for an unknown stationary target $a\in \mathbb{R}$ with known distribution $\mu$. The searcher begins at the origin and performs Brownian motion with diffusion constant $D$. The searcher is also armed with an exponential clock with spatially dependent rate $r=r\left(\cdot \right)$, so that if it has failed to locate the target by the time the clock rings, then its position is reset to the origin and it continues its search anew from there. Denote the position of the searcher at time $t$ by $X\left(t\right)$. Let $E_0^{\left(r\right)}$ denote expectations for the process $X\left(\cdot \right)$. The search ends at time $T_a=inf\{t\ge 0:X\left(t\right)=a\}$. The expected time of the search is then ${\int }_{\mathbb{R}}\left(E_0^{\left(r\right)}{T}_a\right)\mu \left(da\right)$. Ideally, one would like to minimize this over all resetting rates $r$. We obtain quantitative growth rates for $E_0^{\left(r\right)}{T}_a$ as a function of $a$ in terms of the asymptotic behavior of the rate function $r$, and also a rather precise dichotomy on the asymptotic behavior of the resetting function $r$ to determine whether $E_0^{\left(r\right)}{T}_a$ is finite or infinite. We show generically that if $r\left(x\right)$ is of the order ${\left|x\right|}^{2l}$, with $l>-1$, then $log{E}_0^{\left(r\right)}{T}_a$ is of the order ${\left|a\right|}^{l+1}$; in particular, the smaller the asymptotic size of $r$, the smaller the asymptotic growth rate of $E_0^{\left(r\right)}{T}_a$. The asymptotic growth rate of $E_0^{\left(r\right)}{T}_a$ continues to decrease when $r\left(x\right)\sim \frac{D\lambda }{x^2}$ with $\lambda >1$; now the growth rate of $E_0^{\left(r\right)}{T}_a$ is more or less of the order ${\left|a\right|}^{\frac{1+\sqrt{1+8\lambda }}{2}}$. Note that this exponent increases to $\infty$ when $\lambda$ increases to $\infty$ and decreases to 2 when $\lambda$ decreases to 1. However, if $\lambda =1$, then $E_0^{\left(r\right)}{T}_a=\infty$, for $a\ne 0$. Our results suggest that for many distributions $\mu$ supported on all of $\mathbb{R}$, a near optimal (or optimal) choice of resetting function $r$ in order to minimize ${\int }_{{\mathbb{R}}^d}\left(E_0^{\left(r\right)}{T}_a\right)\mu \left(da\right)$ will be one which decays quadratically as $\frac{D\lambda }{x^2}$ for some $\lambda >1$. We also give explicit, albeit rather complicated, variational formulas for ${inf}_{r\gneqq 0}{\int }_{{\mathbb{R}}^d}\left(E_0^{\left(r\right)}{T}_a\right)\mu \left(da\right)$. For distributions $\mu$ with compact support, one should set $r=\infty$ off of the support. We also discuss this case.     

A new adaptive trust region algorithm for optimization problems

It is well known that trust region methods are very effective for optimization problems. In this article, a new adaptive trust region method is presented for solving unconstrained optimization problems. The proposed method combines a modified secant equation with the BFGS updated formula and an adaptive trust region radius, where the new trust region radius makes use of not only the function information but also the gradient information. Under suitable conditions, global convergence is proved, and we demonstrate the local superlinear convergence of the proposed method. The numerical results indicate that the proposed method is very efficient.     

Using a virtual skeleton to increase printability of topology optimized design for industry-class applications

This article broadens the scheme previously developed to associate topology optimization with additive manufacturing through the use of a virtual skeleton, consisting in solving the same physical problem with a discrete approach and then with a continuum one. This procedure for 3D designs is applied to various domain geometries, to demonstrate its pertinence on high-resolution industrial cases. An algorithm searching for the best printing direction, exploring the solid angle, is also described and validated; the surface-shaped presentation of the result allows immediate understanding of the influence of the discrete problem parameters, while its running time is much lower than a unique continuum optimization simulation, which proves the attractiveness of the method. In the three examples studied, the procedure outputs exhibit greater printability than the ones produced by traditional methods in each of the printing direction tested, albeit responsibility is left to the final user to choose his best trade-off. Furthermore, the unprintable zones are readily displayed to be either reworked or supported. Explanations about increase of convergence likelihood on discrete structures despite the geometry complexity of an industrial application are also provided and demonstrated.     

多目标普朗克极小值优化法的多光谱真温反演研究

光谱发射率是辐射体辐射能力的重要参数，通过光谱发射率可以建立辐射体与黑体的之间的桥梁，从而黑体辐射的相关理论就可以应用于辐射体。采用普朗克公式，光谱高温计的每一个光谱通道可以构成一个方程，这个方程中包含有真温、亮度温度和光谱发射率。对于N个光谱通道可以构成N个方程，这N个方程中也包含一个真温、N个亮度温度和N个光谱发射率，其中亮度温度是已知量，真温和光谱发射率是未知量。由于方程组是欠定的，理论上存在着大量的解。为了求解这个方程组常需要假设光谱发射率与波长和温度之间的数学模型，使方程组未知数的个数降为N个，实现真温的求解。当光谱发射率与波长或温度之间的规律被正确获得后，多光谱辐射测温法才能反演出正确的真温。通过对上述较为常用两种光谱发射率模型的分析可知，这两种方法的基本思想都是试图找到光谱发射率与波长或温度之间的函数关系，确立光谱发射率与波长或温度之间数学模型。用含有波长或温度的表达式代替光谱发射率，实现方程的求解。由于光谱发射率具有一定的不确定性，假设的光谱发射率模型与实际光谱发射率的变化之间存在一定的差异，有可能导致真温反演产生较大的误差。光谱发射率与波长或温度之间的数学模型是需要通过大量的实验和经验才能获得的，而且这种数学模型通用性较差，尤其是当待测辐射体发生改变时，这种数学模型也就失去了意义。为了解决多光谱高温计在实际测量中存在的问题，找到一种无需假定光谱发射率与波长或温度之间数学模型而且又具有一定通用性的多光谱真温反演方法成为一种迫切的需要。为此，将优化的思想引入到了多光谱求解过程中，将多光谱真温的求解问题转化为多目标普朗克极小值优化(MMP)问题，从而不再需要建立光谱发射率与波长或温度之间的数学模型，降低了系统的复杂性与难度。该方法以普朗克公式和光谱发射率之间的等式约束条件为基础，构造了六个目标函数，实现了真温的求解。新方法在反演精度上得到了较大幅度的提高，仿真数据的误差都小于1%。借助于以往的真实测量数据，利用多目标普朗克极小值优化法实现了真温的反演。     

城市功能区声环境质量监测时间代表性研究

城市功能区声环境质量监测数据是评估我国城市声环境质量水平的重要依据，为科学准确地反映城市功能区声环境质量水平，应选取合理的、具有代表性的监测时间开展监测。本文以某地4个典型的功能区声环境质量监测点位1年的连续监测数据作为样本，分析了不同功能区点位长期噪声波动特征，并采用Kruskal-Wallis 检验（K-W检验）方法，分别分析了一年中各月之间及一周中各日之间噪声水平的差异性。数据处理及分析结果表明：（1）4个监测点位一年中各月之间噪声水平，特别是夜间等效连续A声级之间具有显著差异，一年监测一次显然难以代表全年声环境质量水平；相比较下，每季度监测一次能大幅提高监测结果代表性，季度内的3个月份各月之间无显著差异的比例占80%以上；（2）对于这4个监测点位,一周中在周二至周五之间监测其结果更具代表性。     

14T MRI大口径全身自屏蔽超导磁体的一种优化设计方法

磁共振成像(MRI)的磁体设计首先是确保中心成像区的场值和均匀性, 二是尽可能减少场值耗散的距离即漏磁5Gs 线. 基于此本文提出了一种线性与非线性规划联合优化的方法. 首先将导体作为基本单元, 在预布置线圈的空间范围内构建二维连续导体网格. 通过线性规划搜索满足磁场约束条件的网格电流分布图. 再将存在电流的网格离散为一个个矩形线圈区域, 在保证场值均匀性、 杂散场5 Gs 线范围以及线圈位置间隔、 导体超导线安全裕度的前提下利用非线性规划, 具体确定各个线圈的轴向和径向位置、 线圈内导体层数和各层匝数以及通电流大小等. 采用这种联合优化方法, 不仅节省优化时间, 还可以自行设计线圈形状有利于工程实现. 文中由此方法给出了14 T MRI 磁体的一种设计方案, 依靠4 组线圈使得45 cm 中心球形成像内不均匀度降低到5 ppm, 而高场耗散的5 Gs 线通过磁体自屏蔽减小到15 m 以内. 满足了设计的要求.     

Ants can orienteer a thief in their robbery

The Thief Orienteering Problem (ThOP) is a multi-component problem that combines features of two classic combinatorial optimization problems: Orienteering Problem and Knapsack Problem. The ThOP is challenging due to the given time constraint and the interaction between its components. We propose an Ant Colony Optimization algorithm together with a new packing heuristic to deal individually and interactively with problem components. Our approach outperforms existing work on more than 90% of the benchmarking instances, with an average improvement of over 300%.     

An artificial hummingbird algorithm based localization with reduced number of reference nodes for wireless sensor networks

Wireless Sensor Networks (WSN) are widely used in recent years due to the advancements in wireless and sensor technologies. Many of these applications require to know the location information of nodes. This information is useful to understand the collected data and to act on them. Existing localization algorithms make use of a few reference nodes for estimating the locations of sensor nodes. But, the positioning and utilization of reference nodes increase the cost and complexity of the network. To reduce the dependency on reference nodes, in this paper, we have developed a novel optimization based localization method using only two reference nodes for the localization of the entire network. This is achieved by reference nodes identifying a few more nodes as reference nodes by the analysis of the connectivity information. The sensor nodes then use the reference nodes to identify their locations in a distributive manner using Artificial Hummingbird Algorithm (AHA). We have observed that the localization performance of the reported algorithm at a lower reference node ratio is comparable with other algorithms at higher reference node ratios.