Search results for: production optimization

Authors: H. E. Keshta, A. A. Ali

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Three phase grid-tied inverters are widely employed in micro-grids (MGs) as interphase between DC and AC systems. These inverters are usually controlled through standard decoupled d–q vector control strategy based on proportional integral (PI) controllers. Recently, advanced meta-heuristic optimization techniques have been used instead of deterministic methods to obtain optimum PI controller parameters. This paper provides a comparative study between the performance of the global Porcellio Scaber algorithm (GPSA) based PI controller and Manta Ray foraging optimization (MRFO) based PI controller.

Keywords: micro-grids, optimization techniques, grid-tied inverter control, PI controller

Procedia PDF Downloads 132

Authors: Bita Payami-Shabestari, Dariush Eslami

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The purpose of this paper is to develop a multi-product economic production quantity model under vendor management inventory policy and restrictions including limited warehouse space, budget, and number of orders, average shortage time and maximum permissible shortage. Since the “costs” cannot be predicted with certainty, it is assumed that data behave under uncertain environment. The problem is first formulated into the framework of a bi-objective of multi-product economic production quantity model. Then, the problem is solved with three multi-objective decision-making (MODM) methods. Then following this, three methods had been compared on information on the optimal value of the two objective functions and the central processing unit (CPU) time with the statistical analysis method and the multi-attribute decision-making (MADM). The results are compared with statistical analysis method and the MADM. The results of the study demonstrate that augmented-constraint in terms of optimal value of the two objective functions and the CPU time perform better than global criteria, and goal programming. Sensitivity analysis is done to illustrate the effect of parameter variations on the optimal solution. The contribution of this research is the use of random costs data in developing a multi-product economic production quantity model under vendor management inventory policy with several constraints.

Keywords: economic production quantity, random cost, supply chain management, vendor-managed inventory

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Authors: Emna Mhedhbi, Claudio Fuentes Grunewald

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According to preliminary results of DAB-KSA project and considering the current 0.09-ha microalgae pilot plant facilities, we can produce 2.6 tons/year of microalgae biomass for proteins applications in animal feeds in KSA. By 2030, our projections are to reach 65,940,593.4 tons deploying 100.000 ha's production plants. We also have assessed the energy cost (industrial) in KSA (€0.061/kWh) and compared to (€0.32/kWh)in Germany, we can argue a clear lower OPEX for microalgae biomass production cost in KSA.

Keywords: microalgae, feed production, bioprocess, fishmeal

Procedia PDF Downloads 187

Authors: M. I. Abdelhamid, A. O. Ghallab, R. S. Ettouney, M. A. El-Rifai

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An optimization scheme based on COM server is suggested for communication between Genetic Algorithm (GA) toolbox of MATLAB and Aspen HYSYS. The structure and details of the proposed framework are discussed. The power of the developed scheme is illustrated by its application to the optimization of a recently developed natural gas liquefaction process in which Aspen HYSYS was used for minimization of the power consumption by optimizing the values of five operating variables. In this work, optimization by coupling between the GA in MATLAB and Aspen HYSYS model of the same process using the same five decision variables enabled improvements in power consumption by 3.3%, when 77% of the natural gas feed is liquefied. Also on inclusion of the flow rates of both nitrogen and carbon dioxide refrigerants as two additional decision variables, the power consumption decreased by 6.5% for a 78% liquefaction of the natural gas feed.

Keywords: stranded gas liquefaction, genetic algorithm, COM server, single nitrogen expansion, carbon dioxide pre-cooling

Procedia PDF Downloads 448

Authors: Prakash Kattire, Rahul Pathare, Nilesh Tawde

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A canopy is an overhead roof structure generally used at the entrance of a building to provide shelter from rain and sun and may also be used for decorative purposes. In this paper, the canopy structure to cover the conveyor line has been studied. Existing most of the canopy structures are made of steel and glass, which makes a heavier structure, so the purpose of this study is to weight and cost optimization of the canopy. To achieve this goal, the materials of construction considered are Polyvinyl chloride (PVC) natural composite, Fiber Reinforced Plastic (FRP), and Structural steel Fe250. Designing and modeling were done in Solid works, whereas Altair Inspire software was used for the optimization of the structure. Through this study, it was found that there is a total 10% weight reduction in the structure with sufficient reserve for structural strength.

Keywords: canopy, composite, FRP, PVC

Procedia PDF Downloads 146

Authors: Sai Siddharth S., Prasanna Kumar G. M., Alagarsamy R.

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This research introduces an alternative approach to urban road maintenance by utilizing Blended Wing Body (BWB) design and Vertical Takeoff and Landing (VTOL) drones. The integration of this aerospace innovation, combining blended wing efficiency with VTOL maneuverability, aims to optimize fuel consumption and explore versatile applications in solving urban problems. A few problems are discussed along with optimization of the design and comparative study with other drone configurations.

Keywords: design optimization, CFD, CAD, VTOL, blended wing body

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Authors: Hameed Rukayat Opeyemi, Pericles Pilidis, Pagone Emanuele

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An infinitesimal increase in performance can have immense reduction in operating and capital expenses in a power generation system. Therefore, constant studies are being carried out to improve both conventional and novel power cycles. Globally, power producers are constantly researching on ways to minimize emission and to collectively downsize the total cost rate of power plants. A substantial spurt of developmental technologies of low carbon cycles have been suggested and studied, however they all have their limitations and financial implication. In the area of carbon abatement in power plants, three major objectives conflict: The cost rate of the plant, Power output and Environmental impact. Since, an increase in one of this parameter directly affects the other. This poses a multi-objective problem. It is paramount to be able to discern the point where improving one objective affects the other. Hence, the need for a Pareto-based optimization algorithm. Pareto-based optimization algorithm helps to find those points where improving one objective influences another objective negatively and stops there. The application of Pareto-based optimization algorithm helps the user/operator/designer make an informed decision. This paper sheds more light on areas that multi-objective optimization has been applied in carbon abatement technologies in the last five years, developments and prospects.

Keywords: gas turbine, low carbon technology, pareto optimal, multi-objective optimization

Procedia PDF Downloads 791

Authors: Supriya Chatla, Anjana Male, Srikala Kamireddy

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L-asparaginase (E.C.3.5.1.1) is an anti-cancer enzyme that has been purified and characterized for decades to study and evaluate its anti-carcinogenic activity against Hodgkin’s lymphoma. The present investigation deals with screening, isolation and optimization of L-asparaginase giving fungal strain of soil samples from different areas of AP, India. L-Aspariginase activity was estimated on the basis of the pink color surrounding the growing colony. A total of 132 colonies were screened and isolated from different samples. Based on the zone diameter, L-asparaginase activity is determined, L- asparaginase activity is optimized at 28oc and Immobilized Aspariginase had more potency than the free enzymes.

Keywords: aspariginase, anticancer enzyme, Isolation, optimization

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Authors: Meziane Rachid, Boufala Seddik, Hamzi Amar, Amara Mohamed

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Today’s energy industry seeks maximum benefit with maximum reliability. In order to achieve this goal, design engineers depend on reliability optimization techniques. This work uses a harmony search algorithm (HS) meta-heuristic optimization method to solve the problem of wind-Solar-Gas power systems design optimization. We consider the case where redundant electrical components are chosen to achieve a desirable level of reliability. The electrical power components of the system are characterized by their cost, capacity and reliability. The reliability is considered in this work as the ability to satisfy the consumer demand which is represented as a piecewise cumulative load curve. This definition of the reliability index is widely used for power systems. The proposed meta-heuristic seeks for the optimal design of series-parallel power systems in which a multiple choice of wind generators, transformers and lines are allowed from a list of product available in the market. Our approach has the advantage to allow electrical power components with different parameters to be allocated in electrical power systems. To allow fast reliability estimation, a universal moment generating function (UMGF) method is applied. A computer program has been developed to implement the UMGF and the HS algorithm. An illustrative example is presented.

Keywords: reliability optimization, harmony search optimization (HSA), universal generating function (UMGF)

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Authors: L. Alejandro Cárdenas, Fernando Herrera, David Nova, Juan Ballesteros

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This paper presents a methodology to optimize the maintenance of grid-connected photovoltaic systems, considering the cleaning and module replacement periods based on an asset management strategy. The methodology is based on the analysis of the energy production of the PV plant, the energy feed-in tariff, and the cost of cleaning and replacement of the PV modules, with the overall revenue received being the optimization variable. The methodology is evaluated as a case study of a 5.6 kWp solar PV plant located on the Bogotá campus of the Universidad Nacional de Colombia. The asset management strategy implemented consists of assessing the PV modules through visual inspection, energy performance analysis, pollution, and degradation. Within the visual inspection of the plant, the general condition of the modules and the structure is assessed, identifying dust deposition, visible fractures, and water accumulation on the bottom. The energy performance analysis is performed with the energy production reported by the monitoring systems and compared with the values estimated in the simulation. The pollution analysis is performed using the soiling rate due to dust accumulation, which can be modelled by a black box with an exponential function dependent on historical pollution values. The pollution rate is calculated with data collected from the energy generated during two years in a photovoltaic plant on the campus of the National University of Colombia. Additionally, the alternative of assessing the temperature degradation of the PV modules is evaluated by estimating the cell temperature with parameters such as ambient temperature and wind speed. The medium-term energy decrease of the PV modules is assessed with the asset management strategy by calculating the health index to determine the replacement period of the modules due to degradation. This study proposes a tool for decision making related to the maintenance of photovoltaic systems. The above, projecting the increase in the installation of solar photovoltaic systems in power systems associated with the commitments made in the Paris Agreement for the reduction of CO2 emissions. In the Colombian context, it is estimated that by 2030, 12% of the installed power capacity will be solar PV.

Keywords: asset management, PV module, optimization, maintenance

Procedia PDF Downloads 52

Authors: Anna Ouskova Leonteva, Michel Risser, Anne Jeannin-Girardon, Pierre Parrend, Pierre Collet

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This paper proposes an efficient optimization tool for an active magnetic regenerator (AMR) model, operating in two modes: magnetic refrigeration system (MRS) and thermo-magnetic generator (TMG). The aim of this optimizer is to improve the design of the AMR by applying a multi-physics multi-scales numerical model as a core of evaluation functions to achieve industrial requirements for refrigeration and energy conservation systems. Based on the multi-objective non-dominated sorting genetic algorithm 3 (NSGA3), it maximizes four different objectives: efficiency and power density for MRS and TMG. The main contribution of this work is in the simultaneously application of a CPU-parallel NSGA3 version to the AMR model in both modes for studying impact of control and design parameters on the performance. The parametric study of the optimization results are presented. The main conclusion is that the common (for TMG and MRS modes) optimal parameters can be found by the proposed tool.

Keywords: ecological refrigeration systems, active magnetic regenerator, thermo-magnetic generator, multi-objective evolutionary optimization, industrial optimization problem, real-world application

Procedia PDF Downloads 114

Authors: A. Peniak, J. Makarovič, P. Rafajdus, P. Dúbravka

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The purpose of this work is to optimize a Switched Reluctance Motor (SRM) for an automotive application, specifically for a fully electric car. A new optimization approach is proposed. This unique approach transforms automotive customer requirements into an optimization problem, based on sound knowledge of a SRM theory. The approach combines an analytical and a finite element analysis of the motor to quantify static nonlinear and dynamic performance parameters, as phase currents and motor torque maps, an output power and power losses in order to find the optimal motor as close to the reality as possible, within reasonable time. The new approach yields the optimal motor which is competitive with other types of already proposed motors for automotive applications. This distinctive approach can also be used to optimize other types of electrical motors, when parts specifically related to the SRM are adjusted accordingly.

Keywords: automotive, drive system, electric car, finite element method, hybrid car, optimization, switched reluctance motor

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Authors: Yassir Abdelrazig, Ren Moses

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Roadway planning and design is a very complex process involving five key phases before a project is completed; planning, project development, final design, right-of-way, and construction. The planning phase for a new roadway transportation project is a very critical phase as it greatly affects all latter phases of the project. A location study is usually performed during the preliminary planning phase in a new roadway project. The objective of the location study is to develop alignment alternatives that are cost efficient considering land acquisition and construction costs. This paper describes a methodology to develop optimal preliminary roadway alignments utilizing spatial-data. Four optimization criteria are taken into consideration; roadway length, land cost, land slope, and environmental impacts. The basic concept of the methodology is to convert the proposed project area into a grid, which represents the search space for an optimal alignment. The aforementioned optimization criteria are represented in each of the grid’s cells. A spatial-data optimization technique is utilized to find the optimal alignment in the search space based on the four optimization criteria. Two case studies for new roadway projects in Duval County in the State of Florida are presented to illustrate the methodology. The optimization output alignments are compared to the proposed Florida Department of Transportation (FDOT) alignments. The comparison is based on right-of-way costs for the alignments. For both case studies, the right-of-way costs for the developed optimal alignments were found to be significantly lower than the FDOT alignments.

Keywords: gemoetric design, optimization, planning, roadway planning, roadway design

Procedia PDF Downloads 338

Authors: Paffrath Meinhard, Zhou Yayun, Guo Yiqing, Ertl Harald

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Winding geometry design is an important part of power transformer electrical design. Conventionally, the winding geometry is designed manually, which is a time-consuming job because it involves many iteration steps in order to meet all cost, manufacturing and electrical requirements. Here a method is presented which automatically generates the winding geometry for given user parameters and allows the user to interactively set and change parameters. To achieve this goal, the winding problem is transferred to a mixed integer nonlinear optimization problem. The relevant geometrical design parameters are defined as optimization variables. The cost and other requirements are modeled as constraints. For the solution, a stochastic ant colony optimization algorithm is applied. It is well-known, that an optimizer can get stuck in a local minimum. For the winding problem, we present efficient strategies to come out of local minima, furthermore a reduced variable search range helps to accelerate the solution process. Numerical examples show that the optimization result is delivered within seconds such that the user can interactively change the variable search area and constraints to improve the design.

Keywords: ant colony optimization, mixed integer nonlinear programming, power transformer, winding design

Procedia PDF Downloads 380

Authors: Laidi Maamar, Achwak Madani, Abdellah El Ahdj Abdellah

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The aim of this work is to use Support Vector regression (SVR) combined with dragonfly, firefly, Bee Colony and particle swarm Optimization algorithm to predict global solar radiation on horizontal surfaces in some cities in Algeria. Combining these optimization algorithms with SVR aims principally to enhance accuracy by fine-tuning the parameters, speeding up the convergence of the SVR model, and exploring a larger search space efficiently; these parameters are the regularization parameter (C), kernel parameters, and epsilon parameter. By doing so, the aim is to improve the generalization and predictive accuracy of the SVR model. Overall, the aim is to leverage the strengths of both SVR and optimization algorithms to create a more powerful and effective regression model for various cities and under different climate conditions. Results demonstrate close agreement between predicted and measured data in terms of different metrics. In summary, SVM has proven to be a valuable tool in modeling global solar radiation, offering accurate predictions and demonstrating versatility when combined with other algorithms or used in hybrid forecasting models.

Keywords: support vector regression (SVR), optimization algorithms, global solar radiation prediction, hybrid forecasting models

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Authors: Barzin Rajabloo, Martin Desilets

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First, an electrochemical model for the reduction of CO₂ into CH₃OH is developed in which mass and charge transfer, reactions at the surface of the electrodes and fluid flow of the electrolyte are considered. This mathematical model is developed in COMSOL Multiphysics® where both secondary and tertiary current distribution interfaces are coupled to consider concentrations and potentials inside different parts of the cell. Constant reaction rates are assumed as the fitted parameters to minimize the error between experimental data and modeling results. The model is validated through a comparison with experimental data in terms of faradaic efficiency for production of CH₃OH, the current density in different applied cathode potentials as well as current density in different electrolyte flow rates. The comparison between model outputs and experimental measurements shows a good agreement. The model indicates the higher hydrogen evolution in comparison with CH₃OH production as well as mass transfer limitation caused by CO₂ concentration, which are consistent with findings in the literature. After validating the model, in the second part of the study, some design parameters of the cell, such as cathode geometry and catholyte/anolyte channel widths, are modified to reach better performance and higher faradaic efficiency of methanol production.

Keywords: carbon dioxide, electrochemical reduction, methanol, modeling

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Authors: Alireza Keshmiri, Shahriar Bagheri, Nan Wu

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This research presents an analytical model for the development of an energy harvester with piezoelectric rings stacked at the boundary of the structure based on the Adomian decomposition method. The model is applied to geometrically non-uniform beams to derive the steady-state dynamic response of the structure subjected to base motion excitation and efficiently harvest the subsequent vibrational energy. The in-plane polarization of the piezoelectric rings is employed to enhance the electrical power output. A parametric study for the proposed energy harvester with various design parameters is done to prepare the dataset required for optimization. Finally, simulation-based optimization technique helps to find the optimum structural design with maximum efficiency. To solve the optimization problem, an artificial neural network is first trained to replace the simulation model, and then, a genetic algorithm is employed to find the optimized design variables. Higher geometrical non-uniformity and length of the beam lowers the structure natural frequency and generates a larger power output.

Keywords: piezoelectricity, energy harvesting, simulation-based optimization, artificial neural network, genetic algorithm

Procedia PDF Downloads 123

Authors: Sourabh Joshi, Geetinder Kaur, Sarabjit Kaur, Gulwatanpreet Singh, Geetika Mannan

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In this paper, we have use an algorithm that able to obtain an optimal solution to travelling salesman problem from a huge search space, quickly. This algorithm is based upon the ant colony optimization technique and employees roulette wheel selection mechanism. To illustrate it more clearly, a program has been implemented which is based upon this algorithm, that presents the changing process of route iteration in a more intuitive way. In the event, we had find the optimal path between hundred cities and also calculate the distance between two cities.

Keywords: ant colony, optimization, travelling salesman problem, roulette wheel selection

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Authors: Mohamed Abdelwahed

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We propose an optimization algorithm for the geometric control of fluid flow. The used approach is based on the topological sensitivity analysis method. It consists in studying the variation of a cost function with respect to the insertion of a small obstacle in the domain. Some theoretical and numerical results are presented in 2D and 3D.

Keywords: sensitivity analysis, topological gradient, shape optimization, stokes equations

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Authors: Bashir Ahmmad, Kensaku Kanomata, Fumihiko Hirose

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The effect of carbon materials on TiO2 for the photocatalytic hydrogen gas production from water/alcohol mixtures was investigated. Single walled carbon nanotubes (SWNTs), multi walled carbon nanotubes (MWNTs), carbon nanofiber (CNF), fullerene (FLN), graphite (GP), and graphite silica (GS) were used as co-catalysts by directly mixing with TiO2. Drastic synergy effects were found with increase in the amount of hydrogen gas by a factor of ca. 150 and 100 for SWNTs and GS with TiO2, repectively. The order of H2 gas production for these carbon materials was SWNTs > GS >> MWNTs > FLN > CNF > GP. To maximize the hydrogen production from SWNTs/TiO2, various parameters of experimental conditions were changed. Also, a comparison between Pt/TiO2, WNTs/TiO2 and GS/TiO2 was made for the amount of H2 gas production. Finally, the recyclability of SWNTs/TiO2 and GS/TiO2 were tested.

Keywords: photocatalysis, carbon materials, alcohol reforming, hydrogen production, titanium oxide

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Authors: Esma Birisci, Ronald McGarvey

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One of the common problems in food service industry is demand uncertainty. This research presents a multi-criteria optimization approach to identify the efficient frontier of points lying between the minimum-waste and minimum-shortfall solutions within uncertain demand environment. It also addresses correlation across demands for items (e.g., hamburgers are often demanded with french fries). Reducing overproduction food waste (and its corresponding environmental impacts) and an aversion to shortfalls (leave some customer hungry) need to consider as two contradictory objectives in an all-you-care-to-eat environment food service operation. We identify optimal production adjustments relative to demand forecasts, demand thresholds for utilization of leftovers, and percentages of demand to be satisfied by leftovers, considering two alternative metrics for overproduction waste: mass; and greenhouse gas emissions. Demand uncertainty and demand correlations are addressed using a kernel density estimation approach. A statistical analysis of the changes in decision variable values across each of the efficient frontiers can then be performed to identify the key variables that could be modified to reduce the amount of wasted food at minimal increase in shortfalls. We illustrate our approach with an application to empirical data from Campus Dining Services operations at the University of Missouri.

Keywords: environmental studies, food waste, production planning, uncertain and correlated demand

Procedia PDF Downloads 372

Authors: Yue Hu, Xilu Zhao, Takao Yamaguchi, Manabu Sasajima, Yoshio Koike, Akira Hara

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This study tried optimization of design parameter of a cone loudspeaker unit as an example of the high flexibility of the products design. We developed an acoustic analysis software program that considers the impact of damping caused by air viscosity. In sound reproduction, it is difficult to each design the parameter of the loudspeaker. To overcome the limitation of the design problem in practice, this paper proposes a new an acoustic analysis algorithm to optimize design the parameter of the loudspeaker. The material character of cone paper and the loudspeaker edge was the design parameter, and the vibration displacement of the cone paper was the objective function. The results of the analysis were compared with the predicted value. They had high accuracy to the predicted value. These results suggest that, though the parameter design is difficult by experience and intuition, it can be performed comparatively easily using the optimization design by the developed acoustic analysis software.

Keywords: air viscosity, loudspeaker, cone paper, edge, optimization

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Authors: F. Bellali, M. Kharroubi, Y. Rady, N. Bourhim

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In Morocco, fish processing industry is an important source income for a large amount of by-products including skins, bones, heads, guts, and scales. Those underutilized resources particularly scales contain a large amount of proteins and calcium. Sardina plichardus scales from resulting from the transformation operation have the potential to be used as raw material for the collagen production. Taking into account this strong expectation of the regional fish industry, scales sardine upgrading is well justified. In addition, political and societal demands for sustainability and environment-friendly industrial production systems, coupled with the depletion of fish resources, drive this trend forward. Therefore, fish scale used as a potential source to isolate collagen has a wide large of applications in food, cosmetic, and biomedical industry. The main aim of this study is to isolate and characterize the acid solubilize collagen from sardine fish scale, Sardina pilchardus. Experimental design methodology was adopted in collagen processing for extracting optimization. The first stage of this work is to investigate the optimization conditions of the sardine scale deproteinization on using response surface methodology (RSM). The second part focus on the demineralization with HCl solution or EDTA. And the last one is to establish the optimum condition for the isolation of collagen from fish scale by solvent extraction. The advancement from lab scale to pilot scale is a critical stage in the technological development. In this study, the optimal condition for the deproteinization which was validated at laboratory scale was employed in the pilot scale procedure. The deproteinization of fish scale was then demonstrated on a pilot scale (2Kg scales, 20l NaOH), resulting in protein content (0,2mg/ml) and hydroxyproline content (2,11mg/l). These results indicated that the pilot-scale showed similar performances to those of lab-scale one.

Keywords: deproteinization, pilot scale, scale, sardine pilchardus

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Authors: Oluwatoyin Joseph Gbadeyan, Sarp Adali, Bright Glen, Bruce Sithole

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Sodium lauryl surfactant concentration optimization, for nanoparticle production, provided the platform for advanced research studies. Different concentrations (0.05 %, 0.1 %, and 0.2 %) of sodium lauryl surfactant was added to snail shells powder during milling processes for producing CaCO3 at smaller particle size. Epoxy nanocomposites prepared at filler content 2 wt.% synthesized with different volumes of sodium lauryl surfactant were fabricated using a conventional resin casting method. Mechanical properties such as tensile strength, stiffness, and hardness of prepared nanocomposites was investigated to determine the effect of sodium lauryl surfactant concentration on nanocomposite properties. It was observed that the loading of the synthesized nano-calcium carbonate improved the mechanical properties of neat epoxy at lower concentrations of sodium lauryl surfactant 0.05 %. Meaningfully, loading of achatina fulica snail shell nanoparticles manufactures, with small concentrations of sodium lauryl surfactant 0.05 %, increased the neat epoxy tensile strength by 26%, stiffness by 55%, and hardness by 38%. Homogeneous dispersion facilitated, by the addition of sodium lauryl surfactant during milling processes, improved mechanical properties. Research evidence suggests that nano-CaCO3, synthesized from achatina fulica snail shell, possesses suitable reinforcement properties that can be used for nanocomposite fabrication. The evidence showed that adding small concentrations of sodium lauryl surfactant 0.05 %, improved dispersion of nanoparticles in polymetrix material that provided mechanical properties improvement.

Keywords: sodium lauryl surfactant, mechanical properties , achatina fulica snail shel, calcium carbonate nanopowder

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Authors: Thomas Weber, Nina Strobel, Thomas Kohne, Eberhard Abele

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In context of the energy transition in Germany, the importance of so-called virtual power plants in the energy supply continues to increase. The progressive dismantling of the large power plants and the ongoing construction of many new decentralized plants result in great potential for optimization through synergies between the individual plants. These potentials can be exploited by mathematical optimization algorithms to calculate the optimal application planning of decentralized power and heat generators and storage systems. This also includes linear or linear mixed integer optimization. In this paper, procedures for reducing the number of decision variables to be calculated are explained and validated. On the one hand, this includes combining n similar installation types into one aggregated unit. This aggregated unit is described by the same constraints and target function terms as a single plant. This reduces the number of decision variables per time step and the complexity of the problem to be solved by a factor of n. The exact operating mode of the individual plants can then be calculated in a second optimization in such a way that the output of the individual plants corresponds to the calculated output of the aggregated unit. Another way to reduce the number of decision variables in an optimization problem is to reduce the number of time steps to be calculated. This is useful if a high temporal resolution is not necessary for all time steps. For example, the volatility or the forecast quality of environmental parameters may justify a high or low temporal resolution of the optimization. Both approaches are examined for the resulting calculation time as well as for optimality. Several optimization models for virtual power plants (combined heat and power plants, heat storage, power storage, gas turbine) with different numbers of plants are used as a reference for the investigation of both processes with regard to calculation duration and optimality.

Keywords: CHP, Energy 4.0, energy storage, MILP, optimization, virtual power plant

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Authors: Ersilio Tushaj

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The efficient solution that satisfies the optimal condition is an important issue in the structural engineering design problem. The new codes of structural design consist in design methodology that looks after the exploitation of the total resources of the construction material. In recent years some non-deterministic or meta-heuristic structural optimization algorithms have been developed widely in the research community. These methods search the optimum condition starting from the simulation of a natural phenomenon, such as survival of the fittest, the immune system, swarm intelligence or the cooling process of molten metal through annealing. Among these techniques the most known are: the genetic algorithms, simulated annealing, evolution strategies, particle swarm optimization, tabu search, ant colony optimization, harmony search and big bang crunch optimization. In this study, five of these algorithms are applied for the optimum weight design of a steel truss structure with variable geometry but fixed topology. The design process selects optimum distances and size sections from a set of commercial steel profiles. In the formulation of the design problem are considered deflection limitations, buckling and allowable stress constraints. The approach is repeated starting from different initial populations. The design problem topology is taken from an existing steel structure. The optimization process helps the engineer to achieve good final solutions, avoiding the repetitive evaluation of alternative designs in a time consuming process. The algorithms used for the application, the results of the optimal solutions, the number of iterations and the minimal weight designs, will be reported in the paper. Based on these results, it would be estimated, the amount of the steel that could be saved by applying structural analysis combined with non-deterministic optimization methods.

Keywords: structural optimization, non-deterministic methods, truss structures, steel truss

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Authors: Tolulope Aremu

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This paper is based on the idea of using deep learning methodology for optimizing production yield by tuning a few key process parameters in a manufacturing environment. The study was explicitly on how to maximize production yield and minimize operational costs by utilizing advanced neural network models, specifically Long Short-Term Memory and Convolutional Neural Networks. These models were implemented using Python-based frameworks—TensorFlow and Keras. The targets of the research are the precision molding processes in which temperature ranges between 150°C and 220°C, the pressure ranges between 5 and 15 bar, and the material flow rate ranges between 10 and 50 kg/h, which are critical parameters that have a great effect on yield. A dataset of 1 million production cycles has been considered for five continuous years, where detailed logs are present showing the exact setting of parameters and yield output. The LSTM model would model time-dependent trends in production data, while CNN analyzed the spatial correlations between parameters. Models are designed in a supervised learning manner. For the model's loss, an MSE loss function is used, optimized through the Adam optimizer. After running a total of 100 training epochs, 95% accuracy was achieved by the models recommending optimal parameter configurations. Results indicated that with the use of RSM and DOE traditional methods, there was an increase in production yield of 12%. Besides, the error margin was reduced by 8%, hence consistent quality products from the deep learning models. The monetary value was annually around $2.5 million, the cost saved from material waste, energy consumption, and equipment wear resulting from the implementation of optimized process parameters. This system was deployed in an industrial production environment with the help of a hybrid cloud system: Microsoft Azure, for data storage, and the training and deployment of their models were performed on Google Cloud AI. The functionality of real-time monitoring of the process and automatic tuning of parameters depends on cloud infrastructure. To put it into perspective, deep learning models, especially those employing LSTM and CNN, optimize the production yield by fine-tuning process parameters. Future research will consider reinforcement learning with a view to achieving further enhancement of system autonomy and scalability across various manufacturing sectors.

Keywords: production yield optimization, deep learning, tuning of process parameters, LSTM, CNN, precision manufacturing, TensorFlow, Keras, cloud infrastructure, cost saving

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Authors: Zeinab Haji Abolhasani, Romeo Marian, Lee Luong

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This paper and its companions (Part II, Part III) will concentrate on optimizing a class of supply chain problems known as Multi- Commodities Consumer Supply Chain (MCCSC) problem. MCCSC problem belongs to production-distribution (P-D) planning category. It aims to determine facilities location, consumers’ allocation, and facilities configuration to minimize total cost (CT) of the entire network. These facilities can be manufacturer units (MUs), distribution centres (DCs), and retailers/end-users (REs) but not limited to them. To address this problem, three major tasks should be undertaken. At the first place, a mixed integer non-linear programming (MINP) mathematical model is developed. Then, system’s behaviors under different conditions will be observed using a simulation modeling tool. Finally, the most optimum solution (minimum CT) of the system will be obtained using a multi-objective optimization technique. Due to the large size of the problem, and the uncertainties in finding the most optimum solution, integration of modeling and simulation methodologies is proposed followed by developing new approach known as GASG. It is a genetic algorithm on the basis of granular simulation which is the subject of the methodology of this research. In part II, MCCSC is simulated using discrete-event simulation (DES) device within an integrated environment of SimEvents and Simulink of MATLAB® software package followed by a comprehensive case study to examine the given strategy. Also, the effect of genetic operators on the obtained optimal/near optimal solution by the simulation model will be discussed in part III.

Keywords: supply chain, genetic algorithm, optimization, simulation, discrete event system

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Authors: Oğuzhan Hasançebi, Saeid Kazemzadeh Azad

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Keywords: structural design optimization, optimal sizing, metaheuristics, self-adaptive step-size search, steel trusses, steel frames

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Authors: Ahmed Elreedy, Ahmed Tawfik

Abstract:

This investigation aims to assess the effect of inoculum to substrate ratio (ISR) and nitrogen to phosphorous balance on simultaneous biohydrogen and methane production from anaerobic decomposition of mono-ethylene glycol (MEG). Different ISRs were applied in the range between 2.65 and 13.23 gVSS/gCOD, whereas the tested N/P ratios were changed from 4.6 to 8.5; both under thermophilic conditions (55°C). The maximum obtained methane and hydrogen yields (MY and HY) of 151.86±10.8 and 22.27±1.1 mL/gCODinitial were recorded at ISRs of 5.29 and 3.78 gVSS/gCOD, respectively. Unlikely, the ammonification process, in terms of net ammonia produced, was found to be ISR and COD/N ratio dependent, reaching its peak value of 515.5±31.05 mgNH4-N/L at ISR and COD/N ratio of 13.23 gVSS/gCOD and 11.56. The optimum HY was enhanced by more than 1.45-fold with declining N/P ratio from 8.5 to 4.6; whereas, the MY was improved (1.6-fold), while increasing N/P ratio from 4.6 to 5.5 with no significant impact at N/P ratio of 8.5. The results obtained revealed that the methane production was strongly influenced by initial ammonia, compared to initial phosphate. Likewise, the generation of ammonia was markedly deteriorated from 535.25±41.5 to 238.33±17.6 mgNH4-N/L with increasing N/P ratio from 4.6 to 8.5. The kinetic study using Modified Gompertz equation was successfully fitted to the experimental outputs (R2 > 0.9761).

Keywords: mono-ethylene glycol, biohydrogen and methane, inoculum to substrate ratio, nitrogen to phosphorous balance, ammonification

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