Thermodynamic analysis and performance optimization of organic rankine cycles for the conversion of low‐to‐moderate grade geothermal heat

The present study considers a thermodynamic analysis and performance optimization of geothermal power cycles. The proposed binary‐cycles operate with moderately low temperature and liquid‐dominated geothermal resources in the range of 110°C to 160°C, and cooling air at ambient conditions of 25°C and 101.3 kPa reference temperature and atmospheric pressure, respectively. A thermodynamic optimization process and an irreversibility analysis were performed to maximize the power output while minimizing the overall exergy destruction and improving the First‐law and Second‐law efficiencies of the cycle. Maximum net power output was observed to increase exponentially with the geothermal resource temperature to yield 16–49 kW per unit mass flow rate of the geothermal fluid for the non‐regenerative organic Rankine cycles (ORCs), as compared with 8–34 kW for the regenerative cycles. The cycle First‐law efficiency was determined in the range of 8–15% for the investigated geothermal binary power cycles. Maximum Second‐law efficiency of approximately 56% was achieved by the ORC with an internal heat exchanger. In addition, a performance analysis of selected pure organic fluids such as R123, R152a, isobutane and n‐pentane, with boiling points in the range of ?24°C to 36°C, was conducted under saturation temperature and subcritical pressure operating conditions of the turbine. Organic fluids with higher boiling point temperature, such as n‐pentane, were recommended for non‐regenerative cycles. The regenerative ORCs, however, require organic fluids with lower vapour specific heat capacity (i.e. isobutane) for an optimal operation of the binary‐cycle. Copyright © 2015 John Wiley & Sons, Ltd.     

The importance of proper economic criteria and process modeling for single- and multi-objective optimizations

This paper provides an overview of the influences that different economic objectives have on the efficiencies of those optimal process designs obtained by using single- and multi-objective optimizations. Optimizations of monetary criteria, like the profit, lead to operationally and environmentally more efficient but economically less attractive designs than optimization of non-monetary economic objectives, like the internal rate of return. The net present value produces compromise designs with intermediate efficiencies and environmental impacts. These differences are significant only if the processes’ mathematical models are sufficiently accurate for establishing appropriate trade-offs between investment and cash flow. The Pareto curves obtained by different economic objectives vary regarding the maximum environmental impacts and in the intervals of the environmental indicators. The composed criteria that combine the economic and environmental indicators into one single objective produce smaller differences between optimum designs that are closer to those designs with minimum possible environmental impacts.     

Efficient multi-criteria optimization on noisy machine learning problems

Recent research revealed that model-assisted parameter tuning can improve the quality of supervised machine learning (ML) models. The tuned models were especially found to generalize better and to be more robust compared to other optimization approaches. However, the advantages of the tuning often came along with high computation times, meaning a real burden for employing tuning algorithms. While the training with a reduced number of patterns can be a solution to this, it is often connected with decreasing model accuracies and increasing instabilities and noise. Hence, we propose a novel approach defined by a two criteria optimization task, where both the runtime and the quality of ML models are optimized. Because the budgets for this optimization task are usually very restricted in ML, the surrogate-assisted Efficient Global Optimization (EGO) algorithm is adapted. In order to cope with noisy experiments, we apply two hypervolume indicator based EGO algorithms with smoothing and re-interpolation of the surrogate models. The techniques do not need replicates. We find that these EGO techniques can outperform traditional approaches such as latin hypercube sampling (LHS), as well as EGO variants with replicates.     

Generalized Pairwise Correlation and method comparison: Impact assessment for JAR attributes on overall liking

In product development using JAR (Just-About-Right) scales, it is important to identify precisely, which direction of a given attribute affects hedonic scores the most. The Generalized Pairwise Correlation Method (GPCM) is a non-parametric one and it is useful to rank JAR variables according to their impact on liking. This is done using appropriate statistical tests: the McNemar’s, the Chi-square, the Conditional Fisher’s and the Williams’ t-test. As GPCM requires one-directional variables, JAR data needs to be transformed based on the dummy variable approach. GPCM gives those attributes in that order, which should be increased/decreased to gain higher consumer liking scores. An order can be created according to the impact on liking, which order determines the development of product attributes, as well. The non-parametric tests incorporated in the method are able to identify smaller differences than other statistical methods. As a result, GPCM identifies more significant product attributes; hence, it can help product development processes even if other methods cannot.     

A simulation-based optimization approach for a semiconductor photobay with automated material handling system

This study addressed the issue of automated material handling systems (AMHS) in the photolithography zone of a 300 mm (12-in.) wafer fab facility. The lithography process accounts for 40–50% of the time required to produce wafers. Therefore, managing the AMHS in the photolithography zone is a challenging task. This paper examines the dispatching rule and the number of vehicles in variable wafer input cases. With a stochastic and complex manufacturing process, a photobay simulation may lead to excessive iterations and wasted computation time. The most frequently used approach for process management in the literature is performance analysis with a model that simulates each alternative for N times. However, this approach becomes time consuming as the number of variables and iterations increases. To address this issue, we use Optimal Computing Budget Allocation (OCBA) and extend OCBA by adding particle swarm optimization (PSO). With this combined approached, the number of iterations of each alternative is determined by OCBA, and the optimal solution in the domain of feasible solutions is identified through PSO. This research provides a useful reference to optimally allocate lithographical resources and the number of iterations with random parameters for both scholars and practitioners. Results demonstrate the superiority of PSO_OCBA in terms of searching quality and robustness.     

Solution of economic load dispatch using hybrid chemical reaction optimization approach

In this paper, a new and efficient optimization technique based on hybridization of chemical reaction optimization (CRO) with differential evolution (DE) is developed and demonstrated to solve the ELD problem with thermal cost function having valve point loading effect together with and without multiple fuel options and with and without considering prohibited operating zone and ramp rate constraint. When valve-point effects, multi-fuel operations and the constraints of prohibited operating zone and ramp rate are taken into account, ELD problem become more complex than conventional ELD problem. To show the priority of the proposed algorithm, it is implemented on six different test systems for solving ELD problems. Comparative studies are carried out to examine the effectiveness of the proposed HCRO-DE approach with conventional DE, CRO and the other algorithms reported in the literature. The simulation results show that the proposed HCRO-DE method is capable of obtaining better quality solutions than DE, CRO and the other well popular optimization techniques.     

An integrated approach to automated innovization for discovering useful design principles: Case studies from engineering

Computational optimization methods are most often used to find a single or multiple optimal or near-optimal solutions to the underlying optimization problem describing the problem at hand. In this paper, we elevate the use of optimization to a higher level in arriving at useful problem knowledge associated with the optimal or near-optimal solutions to a problem. In the proposed innovization process, first a set of trade-off optimal or near-optimal solutions are found using an evolutionary algorithm. Thereafter, the trade-off solutions are analyzed to decipher useful relationships among problem entities automatically so as to provide a better understanding of the problem to a designer or a practitioner. We provide an integrated algorithm for the innovization process and demonstrate the usefulness of the procedure to three real-world engineering design problems. New and innovative design principles obtained in each case should clearly motivate engineers and practitioners for its further application to more complex problems and its further development as a more efficient data analysis procedure.     

An AW-HARIS Based Automated Segmentation of Human Liver Using CT Images

In the digestion of amino acids, carbohydrates, and lipids, as well as protein synthesis from the consumed food, the liver has many diverse responsibilities and functions that are to be performed. Liver disease may impact the hormonal and nutritional balance in the human body. The earlier diagnosis of such critical conditions may help to treat the patient effectively. A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver. The proposed approach can recognize the abnormalities with better accuracy without training, unlike in supervisory procedures requiring considerable computational efforts for training. In the earlier stages, the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation. Then, the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted–-Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information, which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts. It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%. The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.     

A flexible air separation process: 1. Design and steady-state optimizations

The flexible operation of energy-intensive processes, such as cryogenic air separation, has economic potential due to increasing fluctuations of the electricity markets. Multiproduct air separation processes with high ratios of liquid product are very promising for flexible operation due to storable products. We present a process design with an integrated liquefication cycle and liquid assist operation, that facilitates a high liquid product ratio and a flexible process operation. We use a mechanistic dynamic process model in steady-state process optimizations covering the wide operational range of the proposed process. The optimization results show that the power demand can be varied in a range from 3.5 to 28 MW without violating operational constraints by changing the nitrogen and oxygen production rates. Thus, the proposed process is a promising air separation candidate for flexible operation with respect to fluctuating electricity markets.