Predicting the blast-induced vibration velocity using a bagged support vector regression optimized with firefly algorithm

Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.

     

Photocatalytic degradation of Direct Red 23 dye using UV/TiO2: Effect of operational parameters

In this study, the photocatalytic degradation of Direct Red 23 (Scarlet F-4BS) was investigated in UV/TiO2 system. The effect of catalyst loading and pH on the reaction rate was ascertained and optimum conditions for maximum degradation were determined. The results obtained showed that acidic pH is proper for the photocatalytic removal of Direct Red 23. In addition, the effects of several cations (Cu2+, Al3+, Cr3+, and Sn4+) and anions (BiO3(-), SO4(2-), and CN(-)) and C2H5OH were examined in this photocatalytic process. On the order hand, three types of catalysts (Fe2O3, SnO2, and ZnO) were compared with TiO2. After 90 min reaction, the relative decomposition order established was UV/TiO2>UV/SnO2>UV/Fe2O3>UV/ZnO.     

Optimizing dock configuration and staffing in decentralized receiving

In manufacturing facilities, shipping and receiving traditionally have been concentrated in one or two areas of the building. The widespread introduction of just-in-time shipping policies in US manufacturing companies during the past 15 years, as well as the common use of decentralized receiving in the Japanese automobile industry, has motivated the consideration (and occasionally the adoption) of dock configurations to support decentralized receiving (also known as perimeter receiving). By providing multiple access points along the perimeter of the building, decentralized receiving generally reduces the time and cost associated with moving purchased parts to the locations within the facility where they are used, but also results in increased cost of construction, maintenance, and operation for docks and associated truck access/egress, as well as additional interior floorspace requirements. We develop an optimization-based procedure to determine which of several possible dock areas to construct, how many doors or gates each should have, how many unloading and internal material handling staff should be assigned to each group, and which inbound materials each group should handle. We consider the amortized cost of constructing and equipping the docks, the amortized cost of material handling equipment to support the unloaders and material handlers, the cost of operating the material handling equipment, and labor costs for both unloading and internal material handling.     

A Probabilistic Multiperiod Simulation–Optimization Approach for Dynamic Coastal Aquifer Management

Combined simulation–optimization (CSO) schemes are common in the literature to solve different groundwater management problems, and CSO is particularly well-established in the coastal aquifer management literature. However, with a few exceptions, nearly all previous studies have employed the CSO approach to derive static groundwater management plans that remain unchanged during the entire management period, consequently overlooking the possible positive impacts of dynamic strategies. Dynamic strategies involve division of the planning time interval into several subintervals or periods, and adoption of revised decisions during each period based on the most recent knowledge of the groundwater system and its associated uncertainties. Problem structuring and computational challenges seem to be the main factors preventing the widespread implementation of dynamic strategies in groundwater applications. The objective of this study is to address these challenges by introducing a novel probabilistic Multiperiod CSO approach for dynamic groundwater management. This includes reformulation of the groundwater management problem so that it can be adapted to the multiperiod CSO approach, and subsequent employment of polynomial chaos expansion-based stochastic dynamic programming to obtain optimal dynamic strategies. The proposed approach is employed to provide sustainable solutions for a coastal aquifer storage and recovery facility in Oman, considering the effect of natural recharge uncertainty. It is revealed that the proposed dynamic approach results in an improved performance by taking advantage of system variations, allowing for increased groundwater abstraction, injection and hence monetary benefit compared to the commonly used static optimization approach.

     

SHeRo: Scalable hexapod robot for maintenance,repair, and operations

Improper maintenance, repair, and operations of societal centric structures can lead to catastrophic failures that drastically affect global economy, the environment, and everyday life. Due to the remote, cramped and highly irregular environmental nature of these structures, routine manual procedures and operations can be rather tedious, dangerous, and hazardous for humans. Automating maintenance, repair, and operations removes human workers from having to crawl within highly cluttered and constrained spaces, breathing in stale air mixed with fumes from welding or particulate from repair work, and provides higher reliability and consistency in the repair work. This paper introduces SHeRo, a scalable hexapod robot designed for maintenance, repair, and operations within remote, inaccessible, irregular, and hazardous environments. The scalability of the design enhances traditional hexapod robot designs by incorporating two prismatic joints into each leg. A detailed discussion on the design and realization of SHeRo is provided. An analysis on the stability and workspace of SHeRo is presented and a dynamic criterion is developed to integrate the concepts of robot stability and constant orientation workspace into a stable workspace. The analytical solution of the lateral stable workspace of SHeRo is derived along with a metric for comparing stable workspace between different robot configurations. A simulated demonstration and two physical experimental demonstrations are presented showing the advantage of introducing scalability into the hexapod robot design along with the workspace enhancement and flexibility of the scalable hexapod robot.     

A discussion of objective function representation methods in global optimization

Non-convex optimization can be found in several smart manufacturing systems. This paper presents a short review on global optimization (GO) methods. We examine decomposition techniques and classify GO problems on the basis of objective function representation and decomposition techniques. We then explain Kolmogorov’s superposition and its application in GO. Finally, we conclude the paper by exploring the importance of objective function representation in integrated artificial intelligence, optimization, and decision support systems in smart manufacturing and Industry 4.0.     

Solution combustion synthesis of cerium oxide nanoparticles as corrosion inhibitor

In this study, combustion synthesis of cerium oxide nanoparticles was reported using cerium nitrate hexahydrate as starting material as well as urea, glycine, glucose, and citric acid as fuels. The influence of fuel type on structure, microstructure, band gap, and corrosion inhibition was investigated. X-ray diffraction (XRD) patterns and scanning electron microscopy micrographs showed that CeO₂ nanoparticles with different morphologies were obtained depending on the fuel type. Microstructural changes from unreacted gel to sponge-like morphologies were resulted by varying the fuel type from urea, glycine, and glucose to citric acid. In addition to Ce–O bonds, Fourier transform infrared analysis showed carbon bonds of carbonaceous compositions from incomplete combustion which were declined during combustion reaction. Furthermore, corrosion analyses showed that samples synthesized using urea fuel released the most Ce⁺⁴ ions and could have better protection than other samples.     

A Robust Analytical Model of Mobile IP Handoff with Multiple Traffic Profiles

Mobile IP has been developed to provide the continuous information network access to mobile users. The performance of Mobile IP mobility management scheme is dependent on traffic characteristics and user mobility. Consequently, it is important to assess this performance in-depth through these factors. This paper introduces a novel analytical model of handoff management in mobile IP networks. The proposed model focuses on the effect the traffic types and their frame error rates on the handoff latency. It is derived based on general distribution of both successful transmission attempts and the residence time to be applicable in all cases of traffic characteristics and user mobility. The impact of the behavior of wireless connection, cell residence time, probability distribution of transmission time and the handoff time is investigated. Numerical results are obtained and presented for both TCP and UDP traffics. As expected, the reliability of TCP leads to higher handoff latency than UDP traffic. It is shown that, higher values of FER increase the probability of erroneous packet transfer across the link layer. A short retransmission time leads to end the connection most likely in the existing FA; however a long retransmission time leads to a large delivery time. The proposed model is robust in the sense that it covers the impact of all the effective parameters and can be easily extended to any distribution.