In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance... [more]

Copper losses during the Peirce-Smith converter (PSC) operation is of great concern in the copper smelting process. Two primary objectives of the PSC are to produce blister copper with a shorter batch time and to keep the copper losses at a minimum level. Due to the nature of the process, those two objectives are contradictory to each other. Moreover, actions inside the PSC are subject to several operational constraints that make it difficult to develop a scheduling framework for its optimal operation. In this work, a basic but efficient linear multi-period scheduling framework for the PSC is presented that finds the optimal timings of the PSC operations to keep the copper losses and the batch time at a minimum level. An industrial case study is used to illustrate the effectiveness of the proposed framework. This novel solution can be implemented in other smelting processes and used for the design of an inter-PSC scheduling framework.

In recent decades, more and more consumers—referred to as “green consumers”—are willing to incorporate environmentally responsible decisions into their purchasing behaviors. This tendency is particularly notable in the European Union, the USA, and China. From a research perspective, while recent studies on remanufacturing have investigated optimal practices in regard to green consumerism, they have failed to address the flexibility manufacturers are afforded to outsource remanufacturing operations to third parties. In practice, some brand-name manufacturers—such as IBM, Land Rover, and Sun—do indeed outsource their remanufacturing operations. To further our understanding of the implications of differentiated structures for remanufacturing operations under green consumerism, we developed two models: one for a manufacturer undertaking remanufacturing themselves (Model M), and one for a manufacturer outsourcing it to a third-party remanufacturer (Model O). Our results indicate that, for m... [more]

To counteract climate change, the application of renewable energy sources and their efficient use are of crucial importance. In this context, wastewater has also gained increased attention in recent years. For decades, wastewater treatment plants have applied the heat from digester gas combustion to supply internal demands. However, in the context of efficient energy use the question arises: can using high temperature heat for supplying low temperature demand still be considered the best option? This article presents an innovative approach to covering wastewater treatment plant (WWTP) internal demand with low temperature wastewater heat recovery, making thermal energy from digester gas combustion available for feed-in to a local high temperature district heating network. The presented feasibility study was carried out in an Austrian municipality and investigates the heat balance, the economic risk, climatic benefits and the social aspects of the suggested approach. The practical implem... [more]

In view of the current situation that the maturity of enterprise intelligent manufacturing capability is generally low and the information asymmetry in the upstream and downstream of the supply chain is high, taking any supply and demand link in the supply chain as an example, a group of initial demand signals that change nonlinearly over time are divided into intrinsic mode functions and noise residuals with different data characteristics by means of the variational modal decomposition (VMD) algorithm. On the basis of signal denoising and reconstruction, the support vector machine (SVM) algorithm is used to make regression prediction of the reconstructed signal with each intrinsic mode function as sample attribute. Compared with the regression prediction results of the original demand signal, it is verified that the VMD-SVM bullwhip effect weakening model can effectively filter the demand noise generated by each link in the supply chain and improve the accuracy of demand information t... [more]

This paper presents a roadmap performed in 2010 as part of a European project for the modelling of carbon capture and storage technology, and various scenarios with different taxations and permit prices for the CO2 emissions considering the Greek national plans, then the gradual decommissioning of various lignite or other units of electricity power plants. In addition, this study presents a first check, 10 years after its writing, of the current situation of the Greek energy system, regarding the correspondence of the roadmap designed in 2010 to what has been finally executed during this period, including the possibility of other energy sources complimenting or substituting the national strategic energy plans. For this purpose, the integrated MARKAL-EFOM system (TIMES) was employed to model the Greek energy system and evaluate its development over time, until 2040, by analyzing three different scenarios with respect to taxation and permit prices for carbon emissions. The results obtain... [more]

Oil and Gas plants consist of a set of heat exchangers, which are used in recovering the waste heat from product streams to preheat the oil. The heat transfer coefficient of exchangers declines considerably during the operation period due to fouling. Fouling in heat exchangers is a complex phenomenon due to the acceleration of many layers of chemical substances across tubes of heat exchangers resulting from chemical reactions and surface roughness. In this paper, the fouling process was determined as a critical failure in the heat exchanger. Failure is an accelerated fouling layer across the heat exchanger tubes, which can be the reason for the clogging of tubes. Hence, a risk assessment was conducted using the Risk-Based Inspection (RBI) approach to estimate the probability of fouling in heat exchangers. The results showed that the RBI approach can be used successfully to predict the suitable time to shut down the plant and conduct the fouling cleaning process.

Transition from the status quo to more sustainable supply chain management (SSCM) practices is a highly complex and non-linear process with multiple drivers, but also obstacles, on the way. The impending strict regulatory framework, particularly in terms of the environmental dimensions of sustainability development (SD), is single-handedly opening the door to rapid and potentially disruptive change. The research literature on SSCM has increased exponentially over the last decade to meet the mounting demand for information on how to tackle often conflicting sustainability-related requirements while satisfying all internal and external stakeholders. Due to the continuously evolving and wicked nature of SSCM, a limited number of scholars have approached the issue with design thinking problem solving methodologies (DTPSMs). The results of a systematic literature review (SLR) were mirrored with the Evolved Double Diamond (EDD) design process model to formulate a design thinking overview and... [more]

This paper proposes and implements a long-term deterministic capacity expansion model for the co-optimization of electric and natural gas infrastructures. It determines the required investments in generation units, transmission lines and pipelines for meeting future demands, while representing electricity and natural gas flows using DC Power Flow and Weymouth equations, respectively. A Mixed Integer Nonlinear Programming (MINLP) problem is developed, from which a linearized version is derived. A 26 node integrated gas-electric system for the Eastern Region of the United States is used to demonstrate the model’s capabilities. Results show that the model provides an accurate operational representation of the integrated system, and, therefore, enhances the expansion planning process.

Forests can be exploited for obtaining biomass, which belongs to a bioenergy group with great energy potential that could replace fossil fuels. This article presents a novel procedure to quantify, map and define biomass, which takes into account both environmental and economic issues. With regard to the environment, only the annual growth of tree species is considered, and not the previous stocks. The growth is determined by logarithmic equations through an original procedure supported by a biomass estimator, which represents the amount of biomass generated annually for energy use, and by means of Excel tables, the exploitable biomass values are obtained. Previously, and by using GIS, areas with slopes exceeding 20% are discarded, thus avoiding soil erosion and damage, and in any case that biomass is not extracted for economic reasons. The same procedure is followed, discarding those areas located more than 4 km from forest roads and runways, as transport costs are increased. Finally,... [more]

Korean offshore oil and gas (O&G) mega project contractors have recently suffered massive deficits due to the challenges and risks inherent to the offshore engineering, procurement, and construction (EPC) of megaprojects. This has resulted in frequent prolonged projects, schedule delay, and consequently significant cost overruns. Existing literature has identified one of the major causes of project delays to be the lack of adequate tools or techniques to diagnose the appropriateness and sufficiency of the contract deadline proposed by project owners prior to signing the contract in the bid. As such, this paper seeks to propose appropriate or correct project durations using the research methodology of text mining for bid documents. With the emergence of ‘big data’ research, text mining has become an acceptable research strategy, having already been utilized in various industries including medicine, legal, and securities. In this study the scope of work (SOW), as a main part of EPC contr... [more]

Installation of wind-turbine blades on monopile-type offshore wind turbines is a demanding task. Typically, a jack-up crane vessel is used, and blades are individually lifted from the vessel deck and docked with the preinstalled hub. During the process of mating, large relative motions are developed between the hub and root due to combined effects of wind-generated blade-root responses and wave-generated monopile vibrations. This can cause impact loads at the blade root and induce severe damages at the blade-root connection. Such events are highly likely to cause the failure of the mating task, while affecting the subsequent activities, and thus require competent planning. The purpose of this paper is to present a probabilistic response-based methodology for estimating the allowable sea states for planning a wind-turbine blade-mating task, considering impact risks with the hub as the hazardous event. A case study is presented where the installation system consisting of blade-lift and m... [more]

Passive distribution networks are being converted into active ones by incorporating distributed means of energy generation, consumption, and storage, and the formation of so-called microgrids (MGs). As the next generation of MGs, reconfigurable microgrids (RMGs) are still in early phase studies, and require further research. RMGs facilitate the integration of distributed generators (DGs) into distribution systems and enable a reconfigurable network topology by the help of remote-controlled switches (RCSs). This paper proposes a day-ahead operational scheduling framework for RMGs by simultaneously making an optimal reconfiguration plan and dispatching controllable distributed generation units (DGUs) considering power loss minimization as an objective. A hybrid approach combining conventional particle swarm optimization (PSO) and selective PSO (SPSO) methods (PSO&SPSO) is suggested for solving this combinatorial, non-linear, and NP-hard complex optimization problem. PSO-based methods are... [more]

With the wide application of distributed generation (DG) and the rapid development of alternating current/direct current (AC/DC) hybrid microgrids, the optimal planning of distributed generation connecting to AC/DC distribution networks/microgrids has become an urgent problem to resolve. This paper presents a collaborative planning method for distributed generation access to AC/DC distribution (micro) grids. Based on the grid structure of the AC/DC distribution network, the typical interconnection structure of the AC/DC hybrid microgrid and AC/DC distribution network is designed. The optimal allocation models of distributed power supply for the AC/DC distribution network and microgrid are established based on analytical target cascading. The power interaction between the distribution network and microgrid is used to establish a coupling relationship, and the augmented Lagrangian penalty function is used to solve the collaborative programming problem. The results of distributed power su... [more]

The purpose of this research is to develop a mixed integer linear programming model for optimization of a distributed energy system integrated with electricity network. In this model, the optimal configuration of the selected equipment and dynamic intelligent control of the hourly electricity interchange among end-users were determined. The multi-objective function was to maximize the total cost saving and pollutant emission reduction. As an illustrative example, the model was applied to a neighborhood level containing hotel, office and residential buildings in Dalian, China. According to the results, with the installation of the electricity network, the load rate of the power generating unit in the hotel and office were improved and the power generate unit (PGU) often operated at full loads during the daytime with the surplus electricity distributed to the residential buildings. Furthermore, the overall performance was enhanced leading to a more than 30−40% reduction compared to the o... [more]

In this paper, we address the problem of minimizing the total daily energy cost in a smart residential building composed of multiple smart homes with the aim of reducing the cost of energy bills and the greenhouse gas emissions under different system constraints and user preferences. As the household appliances contribute significantly to the energy consumption of the smart houses, it is possible to decrease electricity cost in buildings by scheduling the operation of domestic appliances. In this paper, we propose an optimization model for jointly minimizing electricity costs and CO₂ emissions by considering consumer preferences in smart buildings that are equipped with distributed energy resources (DERs). Both controllable and uncontrollable tasks and DER operations are scheduled according to the real-time price of electricity and a peak demand charge to reduce the peak demand on the grid. We formulate the daily energy consumption scheduling problem in multiple smart homes from econom... [more]

Water demand forecasting applies data supports for the scheduling and decision-making of urban water supply systems. In this study, a new dual-scale deep belief network (DSDBN) approach for daily urban water demand forecasting was proposed. Original daily water demand time series was decomposed into several intrinsic mode functions (IMFs) and one residue component with ensemble empirical mode decomposition (EEMD) technique. Stochastic and deterministic terms were reconstructed through analyzing the frequency characteristics of IMFs and residue using generalized Fourier transform. The deep belief network (DBN) model was used for prediction using the two feature terms. The outputs of the double DBNs are summed as the final forecasting results. Historical daily water demand datasets from an urban waterworks in Zhuzhou, China, were investigated by the proposed DSDBN model. The mean absolute percentage error (MAPE), normalized root-mean-square error (NRMSE), correlation coefficient (CC) and... [more]

Scheduling of multi-microgrids (MMGs) is one of the important tasks in MMG operation and it faces new challenges as the integration of demand response (DR) programs and renewable generation (wind and solar) sources increases. In order to address these challenges, robust optimization (RO)-based scheduling has been proposed in this paper considering uncertainties in both renewable energy sources and forecasted electric loads. Initially, a cost minimization deterministic model has been formulated for the MMG system. Then, it has been transformed to a min-max robust counterpart and finally, a traceable robust counterpart has been formulated using linear duality theory and Karush⁻Kuhn⁻Tucker (KKT) optimality conditions. The developed model provides immunity against the worst-case realization within the provided uncertainty bounds. Budget of uncertainty has been used to develop a trade-off between the conservatism of solution and probability of unfeasible solution. The effect of uncertainty... [more]

This work presents a risk-averse stochastic programming model for the optimal planning of hybrid electrical energy systems (HEES), considering the regulatory policy applied to distribution systems in Brazil. Uncertainties associated with variables related to photovoltaic (PV) generation, load demand, fuel price for diesel generation and electricity tariff are considered, through the definition of scenarios. The conditional value-at-risk (CVaR) metric is used in the optimization problem to consider the consumer’s risk propensity. The model determines the number and type of PV panels, diesel generation, and battery storage capacities, in which the objective is to minimize investment and operating costs over the planning horizon. Case studies involving a large commercial consumer are carried out to evaluate the proposed model. Results showed that under normal conditions only the PV system is viable. The PV/diesel system tends to be viable in adverse hydrological conditions for risk-averse... [more]

The hydropower unit commitment (HUC) is often indirectly considered by combining individual units in a hydropower plant into a plant-based generator to alleviate the dimensional difficulty in short-term hydropower scheduling of cascaded reservoirs. This work presents three procedures to combine operating zones of units in a hydropower plant into the plant-based operating zones, including an exhaustive method (M01) that enumerates all the possible combinations, a discrete method (M03) that investigates discrete values to see if they are in an operating zone, and a bound oriented method (M04) that explores new operating zones with an optimization solver. The procedures are compared with a previous one (M02) that merges the operating zones of a unit one by one in order. The experiments of the methods in 11 case studies involving a variety of hydropower plants reveal that the second method (M02) should always be recommended due to its strength, which is even more prominent than any other m... [more]

The cost of electricity and gas has a direct influence on the everyday routines of people who rely on these resources to keep their businesses running. However, the value of electricity is strongly related to spot market prices, and the arrival of winter and increased energy use owing to the demand for heating can lead to an increase in energy prices. Approaches to forecasting energy costs have been used in recent years; however, existing models are not yet robust enough due to competition, seasonal changes, and other variables. More effective modeling and forecasting approaches are required to assist investors in planning their bidding strategies and regulators in ensuring the security and stability of energy markets. In the literature, there is considerable interest in building better pricing modeling and forecasting frameworks to meet these difficulties. In this context, this work proposes combining seasonal and trend decomposition utilizing LOESS (locally estimated scatterplot smoo... [more]

In order to cope with the efficient consumption and flexible regulation of resource scarcity due to grid integration of renewable energy sources, a scheduling strategy that takes into account the coordinated interaction of source, grid, load, and storage is proposed. In order to improve the accuracy of the dispatch, a BP neural network approach modified by a genetic algorithm is used to predict renewable energy sources and loads. The non-convex, non-linear optimal dispatch model of the distribution grid is transformed into a mixed integer programming model with optimal tides based on the second-order cone relaxation, variable substitution, and segmental linearization of the Big M method. In addition, the uncertainty of distributed renewable energy output and the flexibility of load demand re-response limit optimal dispatch on a single time scale, so the frequency of renewable energy and load forecasting is increased, and an optimal dispatch model with complementary time scales is devel... [more]

Conventional energy system models have limitations in evaluating complex choices for transitioning to low-carbon energy systems and preventing catastrophic climate change. To address this challenge, we propose a model that allows for the exploration of a broader design space. We develop a supervised machine learning surrogate of a capacity expansion model, based on residual neural networks, that accurately approximates the model’s outputs while reducing the computation cost by five orders of magnitude. This increased efficiency enables the evaluation of the sensitivity of the outputs to the inputs, providing valuable insights into system development factors for the Canadian electricity system between 2030 and 2050. To facilitate the interpretation and communication of a large number of surrogate model results, we propose an easy-to-interpret method using an unsupervised machine learning technique. Our analysis identified key factors and quantified their relationships, showing that the... [more]

This paper presents a new energy−economy system modelling approach, developed specifically for energy system planning in non-interconnected islands, aiming for decarbonization. Energy system planning is an essential tool to shape the energy transition to reach carbon neutrality in the medium- and long-term horizon. Islands, as small-scale energy systems, have a limited contribution to the global climate targets, but due to their geographical and natural limitations, they present the potential to become frontrunners in the clean energy transition, especially regarding the efficient use of resources. The specificities and complexities of geographical islands cannot be adequately covered by the available energy modelling tools and new advanced approaches need to be developed to provide the appropriate support in designing the future decarbonized energy systems at insular level. Our methodological approach follows the adaptation and customization of well-established energy−economy modellin... [more]

There is an international consensus that reductions of CO2 emissions are needed in order to reduce global warming. So far, underground aquifer storage of CO2 is the only commercially active option, and it has been so since 1996, when STAOIL started injecting a million tons of CO2 per year into the Utsira formation. Storage of CO2 in the form of solid hydrate is another option that is safer. Injection of CO2 into CH4 hydrate-filled sediments can lead to an exchange in which the in situ CH4 hydrate dissociates and releases CH4. Two types of additives are needed, however, to make this exchange feasible. The primary objective of the first additive is related to hydrodynamics and the need to increase injection gas permeability relative to injection of pure CO2. This type of additive is typically added in amounts resulting in concentration ranges of additive in the order of tens of percentages of CO2/additive mixture. These additives will, therefore, have impact on the thermodynamic properti... [more]