Simultaneous synthesis of a heat exchanger network with multiple utilities using utility substages

Heat exchanger network synthesis (HENS) has progressed by using mathematical programming-based simultaneous methodology. Although various considerations such as non-isothermal mixing and bypass streams are applied to consider real world alternatives in modeling phase, many challenges are faced because of its properties within non-convex mixed-integer nonlinear programming (MINLP). We propose a modified superstructure, which contains a utility substage for use in considering multiple utilities in a simultaneous MINLP model. To improve model size and convergence, fixed utility locations according to temperature and series connections between utilities are suggested. The numbers of constraints, discrete, and continuous variables show that overall model size decreases compared with previous research. Thus, it is possible to expand the feasible search area for reaching the nearest global solution. The model's effectiveness and applications are exemplified by several literature problems, where it is used to deduce a network superior to that of any other reported methodology.     

Comparative evaluation of different statistical tools for the prediction of uniaxial compressive strength of rocks

In this study, uniaxial compressive strength (UCS), unit weight (UW), Brazilian tensile strength (BTS), Schmidt hardness (SHH), Shore hardness (SSH), point load index (Is₅₀) and P-wave velocity (V_p) properties were determined. To predict the UCS, simple regression (SRA), multiple regression (MRA), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) have been utilized. The obtained UCS values were compared with the actual UCS values with the help of various graphs. Datasets were modeled using different methods and compared with each other. In the study where the performance indice PI_at was used to determine the best performing method, MRA method is the most successful method with a small difference. It is concluded that the mean PI_at equal to 2.46 for testing dataset suggests the superiority of the MRA, while these values are 2.44, 2.33, and 2.22 for GEP, ANFIS, and ANN techniques, respectively. The results pointed out that the MRA can be used for predicting UCS of rocks with higher capacity in comparison with others. According to the performance index assessment, the weakest model among the nine model is P7, while the most successful models are P2, P9, and P8, respectively.     

An efficient approach for the siting and sizing problem of distributed generation

This paper deals with the subject of finding the site and size of distributed generation in distribution systems. This problem corresponds to a mixed-integer nonlinear problem which is difficult and hard to solve with classical optimization techniques. Many approaches and with different objective functions have been applied to solve it. In this paper, the problem to solve comprises multiple distributed generation sources and the objective of minimizing power losses and generation costs, both for the distributed and conventional generators. Since it is highly combinatorial, a search space reduction is needed. So, an approximate model is used to reduce the search space of the possible buses where the distributed generation has to be located. Then, for each combination of the reduced space search, a nonlinear equations system is solved by a numerical method to get the size of the distributed generation, checking the voltage limits and the lines’ capacities. To test the method, a comparison between the proposed algorithm and a force brute algorithm is performed on a 69 and 118 bus test systems. The obtained results indicate that this method find the optimal or near optimal solution in a reasonable computation time.     

A procedure for design of hydrogen networks with multiple contaminants☆

It is necessary to reduce hydrogen consumption to meet increasingly strict environmental and product-quality regulations for refinery plants. In this paper, the concentration potential concepts proposed for design of water-using networks are extended to synthesis of hydrogen networks with multiple contaminants. In the de-sign procedure, the precedence of processes is determined by the values of concentration potential of demands. The usage of complementary source pair(s) to reduce utility consumption is investigated. Three case studies are presented to il ustrate the effectiveness of the method. It is shown that the design procedure has clear engineer-ing meaning.     

Power Input Prediction in Agitated Gas‐Liquid Contactors with Non‐coalescent Batch

Volumetric mass transfer coefficients, k_La, just as power input are considered as essential parameters for mechanically agitated gas‐liquid contactors in relation to their optimization and design. The knowledge of power input is crucial for the prediction of other mass transfer characteristics. A power input correlation is created for the industrial design of the process with a non‐coalescent batch that would be appropriate for a broad range of operational conditions. The recommended resulting correlation is able to predict the power input for impellers in industrial‐scale design for a significant scope of operational conditions.     

Detection of Multiple Blockages in Parallelized Microreactors

In a plant consisting of parallelized microreactors (MRs), the product quality is lowered because of a lack of flow uniformity among them when blockage occurs. It is not practical to install sensors in every MR from the viewpoint of cost when detecting the blocked MRs. In the previous study, the multiple blockage detection (MBD) method using a small number of sensors was proposed, but its performance became low when the number of sensors decreased. Here, the conventional algorithm for MBD is improved by considering the process behavior on blockage occurrence, and the effectiveness of the improved algorithm is demonstrated through a numerical case study. The effects of flow distributor types and sensor types on the MBD performance are numerically investigated.     

DEM study of particle motion in novel high-speed seed metering device

This paper presents a numerical study on the motions of seed particles in a novel high-speed seed metering device based on the discrete element method. The effects of key parameters, including inlet velocity of particles, feeding rates and the angle of seed cleaning element, are investigated by a series of controlled numerical experiments. The results show that in the seed filling area of the device, a better match of particle velocity and the tangential linear velocity of the seeding plate can improve the seed filling performance. The number of particles remaining in the metering device is an important factor that affects the device performance. A large number of particles in the device results in more multiple seeding, and a small number causes more leakage seeding. Under the conditions of the seeding plate rotational speed at 194.5 rpm (corresponding to the high ground speed of 14 km/h), feeding rate at ~14 seeds/s can ensure that around 24 seed particles are maintained in the device which generates minimum leakage and multiple seeding. The studies of the effect of the seed cleaning element angle show that an appropriate angle (e.g., 30°) can effectively enhance the clearing ability, increase the uniform stability of particles backflow, and improve the seeding performance.     

High-speed,two-dimensional digital image correlation algorithm using heterogeneous (CPU-GPU) framework

Two-dimensional digital image correlation (2D-DIC) is an experimental technique used to measure in-plane displacement of a test specimen. Real-time measurement of full-field displacement data is challenging due to enormous computational load of the algorithm. In order to improve the computational speed, the focus of recent research works has been on the approach of parallelization across subsets within image pairs using graphics processing unit (GPU). But alternate GPU-based parallelization approaches to improve the performance of this algorithm as per the order of data processing have not been explored. To address this research gap, our method utilizes parallelism within a subset as well as across subsets for each computation step in an iteration cycle. A heterogeneous (CPU-GPU) framework in combination with a pyramid-based initial values estimation for subsets (in parallel) is proposed in this work. The precompute steps of the proposed framework are implemented using CPU, whereas the main iterative steps are realized using GPU. It is demonstrated that the overall computational speed of the proposed heterogeneous framework improves by compared to a sequential CPU-based implementation for a pair of gray-scale images with a resolution of pixels. As an important milestone, feasibility to measure deformations in real time ( 1 s) is manifested in this study.     

Constructing γ-MnO2 hollow spheres with tunable microwave absorption properties

Hollow γ-MnO₂ sphere was obtained by annealing the precursor at 400 °C with different holding time. The influences of different holding time on the morphology and crystalline structure of final products have been discussed in detail, and the microwave absorption properties of the as-products were also investigated. The results exhibited that finer crystalline feature of the γ-MnO₂ and larger pore size in the hollow γ-MnO₂ sphere could be obtained with the extended holding time. The MnO₂/paraffin composites (50 wt% loading) present extraordinary microwave absorption performance, and the minimum reflection loss (RL) values is −51.3 dB at 4.9 GHz with the thickness of 3.5 mm. The excellent electromagnetic absorption properties can be ascribed to the hollow structure, perfect impedance matching behavior and the multiple interface polarization effect.