Batch Production Research Articles

Automated and closed clinical-grade manufacturing protocol produces potent NK cells against neuroblastoma cells and AML blasts

Natural killer (NK) cells are a promising allogeneic immunotherapy option due to their natural ability to kill tumor cells, and due to their apparent safety. This study describes the development of a GMP-compliant manufacturing protocol for the local production of functionally potent NK cells tailored for high-risk acute myeloid leukemia (AML) and neuroblastoma (NBL) patients. Moreover, the quality control strategy and considerations for product batch specifications in early clinical development are described. The protocol is based on the CliniMACS Prodigy platform and Natural Killer Cell Transduction (NKCT) (Miltenyi Biotec). NK cells are isolated from leukapheresis through CD3 depletion and CD56 enrichment, followed by a 12-hour activation with IL-2 and IL-15 cytokines. Three CliniMACS Prodigy processes demonstrated the feasibility and consistency of the modified NKCT process. A three-step process without expansion, however, compromised the NK cell yield. T cells were depleted effectively, indicating excellent safety of the product. Characterization of the NK cells before and after cytokine activation revealed a notable increase in the expression of activation markers, particularly CD69, consistent with enhanced functionality. Intriguingly, the NK cells exhibited increased killing efficacy against patient-derived CD33 + AML blasts and NBL cells in vitro, suggesting a potential therapeutic benefit in AML and NBL.

Pipe production cost management model based on graph theory

The pipe production process is a complex dynamical system with a significant number of production operations and interconnected cost localization centres. Purpose. To develop a mathematical model of the process of handling analytical information of a pipe enterprise using graph theory for the needs of cost management at each production stage. Methodology. Technological flow charts of the production job, where the list of mandatory activities and routing of the operating process are indicated, were used to organize cost accounting by operational centres of their localization with the help of computer modelling elements, namely the graph theory. The work used a complex of research methods, including analysis and scientific synthesis of scientific and technical information; theoretical studies; methods of mathematical and computer modelling, engineering developments. Findings. The cost accounting process is represented by a directed hypergraph. Each production operation is associated with a graph vertex. Each vertex is assigned to a series of interrelated marks: type of technological operation; weight factor; the value of direct costs for the operation; the value of indirect costs for the operation; the expense factor. The initial state of the hypergraph is specified by means of its initial marking, which corresponds to the set cipher of the batch of pipes and its weight. Further execution of the marked hypergraph is performed by running the allowed vertices. Marking of the graph ends if all the information from the technological flow charts of pipe products has been used. Originality. A model of cost management by operational centres is proposed which will allow dividing the prime cost of pipes which differ according to the production technology. It is recommended to rank pipe products within each technologically similar group for an economically feasible allocation of costs between batches of pipes. The proposed process model of processing analytical information of the pipe enterprise using graph theory will allow providing data governance as for the prime cost of each individual batch of pipe products, will contribute to the improvement in the order generation procedure taking into account the acquired information, and will allow making the appropriate adjustments when making management decisions. Practical value. Calculation of expenses for production operations allows one to identify the most cost-intensive of them. Relying on the results of the calculation, the enterprise management has the opportunity to manage costs at any stage of the production process. Moreover, the model allows determining the amount of consumed metal for each production operation, which is relevant for pipe enterprises. It is appropriate to focus further scientific developments in this direction on the possibility of managing non-manufacturing cost with the help of mathematical models in economics to optimize the supply and sales activities of the enterprise.

Visual pattern auto-generation for yarn-dyed plaid fabric based on modified genetic operators

With the aggravated homogenization of the commodity in the increasingly competitive marketplace, the industries have been converted to design and manufacture small batch productions with a wide variety. To reduce the designer’s burden and shorten the design cycle of the product, an auto-generation method for the pattern of yarn-dyed fabric was proposed based on the modified genetic operators. Initially, the design elements to form the pattern were extracted and encoded as a chromosome by natural number encoding. Then, the fabric weave model was built using the matrix of 0 and 1. Based on the color scheme and yarn arrangement, the pattern of yarn-dyed fabric can be generated automatically. The elements can be exchanged to generate new patterns by the genetic operators. Finally, the obtained pattern was imported into the three-dimensional virtual shirt model. Experiments show that the proposed method can help the designer obtain different patterns quickly and effectively, and different design elements can be controlled according to the requirements of the designer. Based on the annual fashion trend, fashionable colors can be involved to realize fast reaction in product design.

Fluoride Concentrations in Different Brands of Toothpaste Marketed in Saudi Arabia.

This study aimed to evaluate the concentrations of total fluoride (TF), total soluble fluoride (TSF) and ionic fluoride (IF) in various toothpaste brands marketed in Saudi Arabia and compare them with the labelled values. Twenty toothpaste brands were randomly selected from supermarkets and hypermarkets in Riyadh, Saudi Arabia. For each brand, two samples from different production batches were purchased and labelled as sample 1 and sample 2. TF and TSF were determined chemically, while IF was measured with fluoride electrodes. The measured fluoride concentrations were compared with the labelled values and the recommended 1000-1500 ppm range for effective caries prevention. In total, 40 toothpaste samples were analysed (20 from each of two batches). For TF, six toothpastes in sample 1 exceeded the standard limit of 1500 ppm, with a range of 2102-2462 ppm, while nine in sample 2 surpassed the limit, ranging from 1538 to 1846 ppm. Conversely, eight toothpastes in sample 1 and two in sample 2 were below the minimum recommended level of 1000 ppm, with ranges of 308-923 ppm and 615-923 ppm, respectively. The TSF ranged from 924 to 2157 ppm in sample 1 and 923-2154 ppm in sample 2. The IF varied from 615 to 2462 ppm in sample 1 and 308-2154 ppm in sample 2. Most toothpaste brands had fluoride levels within the recommended concentration range. However, a significant number exhibited fluoride levels either below the minimum effective dose or exceeding the maximum recommended limit. Fluoride variance in toothpaste demands awareness for dentists and public health to guide optimal caries prevention.

Hybrid Multi-Criteria Decision Making for Additive or Conventional Process Selection in the Preliminary Design Phase

Additive manufacturing (AM) has become a key topic in the manufacturing industry, challenging conventional techniques. However, AM has its limitations, and understanding its convenience despite established processes remains sometimes difficult, especially in preliminary design phases. This investigation provides a hybrid multi-criteria decision-making method (MCDM) for comparing AM and conventional processes. The MCDM method consists of the Best Worst Method (BWM) for the definition of criteria weights and the Proximity Index Value (PIV) method for the generation of the final ranking. The BWM reduces the number of pairwise comparisons required for the definition of criteria weights, whereas the PIV method minimizes the probability of rank reversal, thereby enhancing the robustness of the results. The methodology was validated through a case study, an aerospace bracket. The candidate processes for the bracket production were CNC machining, high-pressure die casting, and PBF-LB/M. The production of the bracket by AM was found to be the optimal choice for small to medium production batches. Additionally, the study emphasized the significance of material selection, process design guidelines, and production batch in the context of informed process selection, thereby enabling technical professionals without a strong AM background in pursuing conscious decisions.

Inhibition Control by Continuous Extractive Fermentation Enhances De Novo 2-Phenylethanol Production by Yeast.

Current microbial cell factory methods for producing chemicals from renewable resources primarily rely on (fed-)batch production systems, leading to the accumulation of the desired product. Industrially relevant chemicals like 2-phenylethanol (2PE), a flavor and fragrance compound, can exhibit toxicity at low concentrations, inhibit the host activity, and negatively impact titer, rate, and yield. Batch liquid-liquid (L-L) In Situ Product Removal (ISPR) was employed to mitigate inhibition effects, but was not found sufficient for industrial-scale application. Here, we demonstrated that continuous selective L-L ISPR provides the solution for maintaining the productivity of de novo produced 2PE at an industrial pilot scale. A unique bioreactor concept called "Fermentation Accelerated by Separation Technology" (FAST) utilizes hydrostatic pressure differences to separate aqueous- and extractant streams within one unit operation, where both production and product extraction take place - allowing for the control of the concentration of the inhibiting compound. Controlled aqueous 2PE levels (0.43 ± 0.02 g kg-1) and extended production times (>100 h) were obtained and co-inhibiting by-product formation was reduced, resulting in a twofold increase of the final product output of batch L-L ISPR approaches. This study establishes that continuous selective L-L ISPR, enabled by FAST, can be applied for more economically viable production of inhibiting products.

Improved quality control of allergen products: Assessing the molecular allergen composition by mass spectrometry.

Natural allergen sources contain a variety of allergens, against which allergic subjects have developed individual sensitization profiles. Ideal allergen products for skin prick testing (SPT) and allergen immunotherapy (AIT) should contain the complete set of allergens of the respective allergen sources to cover all sensitization profiles. However, commercially available allergen products were shown to vary regarding their allergen composition. The qualitative allergen composition of different SPT and AIT products produced from pollen of grasses, birch, mugwort and from house dust mites was assessed by a consistent high-resolution liquid chromatography-coupled tandem mass spectrometry method (LC-MS/MS). All major, mid-tier and most minor allergens were detected in each of the investigated three batches of SPT and AIT products, demonstrating the completeness of the allergen composition and a high degree of batch-to-batch consistency. This is the first study using a single consistent high-resolution LC-MS/MS method to provide solid data on the qualitative allergen composition of SPT and AIT products manufactured from various common allergen sources. The applied method showed high reliability in qualitative batch-to-batch consistency testing and can be performed fast and with high throughput. High-resolution LC-MS/MS is applicable for process development and quality control to ensure market availability of allergen products corresponding to the composition of the respective natural allergen sources.

Enhancing the production and immobilization of cell‐bound lipase from yeast‐like fungus Magnusiomyces capitatusA4C for sustainable biodiesel production in a packed bed reactor

AbstractMagnusiomyces capitatus A4C, a mycelium‐forming and lipase‐producing yeast‐like fungus, was employed in a five‐level factorial design to optimize the collective and interactive influences of carbon, nitrogen and emulsifying sources and their possible effects on cell‐bound lipase (CBL) and cell biomass production. The cell culture of M. capitatus A4C was incubated along with biomass support particles (BSPs) to immobilize the enzyme while anchoring CBL on their surfaces. Among the BSPs tested, CBL immobilized on loofah sponge under optimized conditions showed a substantial hydrolytic activity of 12.7 U mL−1 and a cell‐loading capacity of 0.61 g g−1 of BSPs. Immobilized CBL was applied for biodiesel production via transesterification and esterification. The conversion percentage of triacylglycerides was approximately 100% at 24 h with the addition of water at 1:1 (v/v). The conversion of oleic acid into biodiesel via esterification was 100% at 48 h in the presence of 15% (v/v) isooctane. Further, biodiesel production was scaled up using a packed bed reactor. The batch production of biodiesel in a packed bed reactor through transesterification was 96.2%, with a circulation flow rate of 5.5 mL min−1 for 18 h. On the other hand, oleic acid conversion into biodiesel via esterification was 99.5%, with a circulation flow rate of 5.5 mL min−1 for 24 h. Further investigation revealed that the immobilized biocatalyst exhibited higher stability with esterification (85.3% fatty acid methyl ester) after ten repeated cycles.

Statistical Analysis for Quality Control of Monodispersed Polystyrene Particles Certified Reference Materials

Monodispersed polystyrene latex (PSL) particles serve as certified reference materials (CRMs) for ensuring quality, method validation, and calibration of particle size measuring instruments. They were produced in compliance with ISO 17034:2016 standards. However, limitations arise in the quality control of CRM characteristics due to challenges in synthesizing particles of consistent size across multiple batches. It is widely recognized that variations in particle size homogeneity between batches can occur in the particle manufacturing process even when conducted under identical conditions and ingredients. This paper aims to explain and demonstrate statistical analyses conforming to ISO Guide 35:2017 used for quality control and certifying reference values for monodispersed polystyrene particles. The procedure can be divided into three steps: 1) preliminary check by using dynamic light scattering (DLS), 2) preliminary check by using transmission electron microscopy (TEM), and 3) final homogeneity testing, along with short-term and long-term stability assessment and characterization. The described protocol can control the quality of particles from multiple batch productions by establishing criteria at each step to ensure particle size consistency. The certified value of a 100 nm diameter monodispersed polystyrene particle produced by the National Institute of Metrology, Thailand (NIMT) is 105.5 ± 4.6 nm, acceptable for measurement traceability for testing and calibration services. Keywords: Quality control, polystyrene particles, certified reference materials

Development of Personalised Immediate-Release Gel-Based Formulations Using Semi-Solid Extrusion.

Precision in dosing is crucial for optimizing therapeutic outcomes and preventing overdosing, especially in preterm infants. Traditional manual adjustments to adapt the dose often lead to inaccuracies, contamination risks, and reduced precision. To overcome these challenges, semi-solid extrusion 3D printing was used to create personalised gel-based caffeine dosage forms. The hydrogels, made from agar and hydroxypropyl methylcellulose, demonstrated excellent rheological properties, ensuring uniform extrusion and accurate shape retention during and after printing. This gel formulation allowed for precise adjustments of caffeine volume and content tailored to a neonate weighing 1.36 kg, achieving a recovery of 103.46%, well within acceptable limits. Additionally, three production batches confirmed the process's reproducibility with minimal variability. Forced degradation studies showed that both pure caffeine and caffeine in the gel matrix exhibited similar stability profiles, confirming the drug's chemical integrity. The printed gel dosage forms also displayed immediate-release characteristics, with over 80% of caffeine released within 45 min, highlighting their suitability for rapid therapeutic action. These findings emphasise the potential of SSE 3DP and gel-based formulations to produce personalised drug delivery systems with high precision, reproducibility, and reliability.

Integrating CLSP and GLSP models for optimizing production batch sizes in vulcanized component manufacturing

This study addresses the complex issue of optimizing production batch sizes within a company specializing in manufacturing rubber, thermoplastic, and polyurethane components, all of which undergo vulcanization prior to sale. Through meticulous analysis of the production planning process, it was observed that certain activities could be logically grouped into two sequential stages without compromising information integrity or detail. The initial stage encompasses press activities involving vulcanization and curing, while the next one involves greenhouse operations for post-curing. The commencement of the second stage is contingent upon the completion of all activities in the first stage. Consequently, an effective production plan must concurrently consider both stages. To tackle this, a mathematical model is proposed to address the batch sizing problem within the company, integrating both stages and drawing inspiration from the CLSP (Capacitated Lot Sizing Problem) for the first stage and the subperiod approach derived from GLSP (General Lot-sizing and Scheduling Problem) models for the second stage. The model is solved utilizing the CPLEX solver version 12.8. It incorporates 74 items, spans 20 periods, each with specific demands, and delineates 16 sub-periods within each planning horizon period. Experimentation involving a hypothetical example and subsequent tests utilizing real data from the case study company demonstrate the successful resolution of the problem, yielding enhanced production plans, particularly in terms of cost reduction objectives.

GC/MS Screening of Substances Released from Post-Consumer Recycled HDPE Pellets into 95% Ethanol: Reproducibility and Variation between Production Batches

The use of post-consumer recycled (PCR) plastic materials in sensitive packaging applications, such as for cosmetic products and detergents, requires a clear understanding of the identities and quantities of chemical substances, which they may release into packed products. With many potential sources of and thus different types of potentially releasable substances, a reliable non-targeted screening method is required to assess these materials. Such a method should be readily applicable in industrial practice and provide a realistic estimation of substance release. This investigation focused on the use of gas chromatography/coupled mass spectrometry (GC/MS) to analyze substances, which recycled HDPE (rHDPE) plastic pellets release into 95% ethanol under accelerated testing conditions. The results of the repeated testing of reference samples clearly demonstrated the good reproducibility of the described methodology, with standard deviations of repeated determinations of the total released substance amounts of 6.8–8.1%. The application to several production batches of three commercial rHDPE grades additionally demonstrated that the batch-to-batch variation of substances which rHDPE materials release can be confined to less than 10% of variation of the total detectable substance amount. The described methodology is therefore seen as a pragmatic, repeatable assessment of recycled HDPE plastic batches with a view to substance release.

Glassy Carbon Fiber‐Like Multielectrode Arrays for Neurochemical Sensing and Electrophysiology Recording

AbstractReal‐time measurement of neurochemical and electrophysiological activities in the living brain is vital for advancing neuroscience and understanding brain disorders. Carbon fiber microelectrodes (CFEs) have been widely utilized for in vivo electrochemical detection due to their subcellular size, biocompatibility, and desirable electrochemical properties, and CFE arrays have demonstrated excellent multi‐site chronic sensing of dopamine (DA) and neural activity recording capabilities. However, manual fabrication of CFE arrays lacks reproducibility and batch production capacity. Alternatively, photolithography enables batch fabrication of glassy carbon (GC) multielectrode arrays (GC‐MEAs) with high resolution and reproducibility, but the insertion of planar flexible MEAs poses challenges. In this study, GC fiber‐like (GCF) MEAs are presented and fabricated using photolithography. The GCF MEAs feature fiber‐like GC electrodes with small cross‐sections, facilitating self‐insertion into brain tissue without additional aids. Batch fabrication of GCF MEAs allows for customizable designs with minimal manual intervention. Comparative analysis with CFEs demonstrates improved electrochemical properties of GCF. In vivo experiments in mouse brains confirm the GCF MEAs' ability to measure neurotransmitter concentrations and record neural activities. This novel fabrication approach is promising for multimodal electrical and chemical measurements with minimal footprint, offering significant advancements in neural interface technology for neuroscience research and clinical applications.

Lean Manufacturing Implementation in Developing Countries:Evidence from Jordanian Industrial Companies

In today's fiercely competitive landscape, industries are increasingly embracing lean manufacturing strategies for survival. However, the adoption of lean principles in Jordanian manufacturing sectors remains relatively new. This study aims to evaluate the current state of lean adoption in Jordanian industries, focusing on its techniques, motivations, and challenges. Employing a quantitative methodology, surveys targeted Production, Operation, and Quality Managers in Jordanian organizations. Out of 300 distributed questionnaires, 176 valid responses were received, representing a response rate of 58.7%. The findings indicate a relatively low adoption of lean practices in Jordanian industries, though firms that have implemented lean report significant benefits, particularly in quality enhancement and cost reduction. Key challenges include inadequate training, a scarcity of experts, and complexities in small batch production. While the study's sample size is limited, caution is warranted in interpreting the results. Further empirical research is needed to quantify the performance benefits of lean adoption. This study sheds light on lean adoption in Jordanian manufacturing, informing knowledge development and guiding future efforts to promote lean practices in the industrial landscape.

Microchip gas chromatographic columns dedicated for space exploration: Stationary phase coating, setup optimization and evaluation of column performances

This work is the first part of a project aiming to specifically design gas chromatographic microcolumns for space exploration. In particular, this study explored the functionalization and characterization of silicon/glass microchip gas chromatographic columns designed with a serpentine-shaped channel having an internal square cross-section. This microcolumn can be connected using a robust fluidic manifold with removable capillary connections to a conventional gas chromatograph or implemented in a prototype instrument for space exploration. First, benchtop gas chromatographic system was optimized in terms of injector liner volume, internal diameter (I.D.) of the capillary connections and data frequency of detection to ensure an optimal evaluation of column efficiency. Junction capillaries of 100 μm I.D., a liner of 1.2 mm I.D. and a detection acquisition frequency of 100 Hz were found to be the optimal set-up and parameters. Moreover, the stationary phase coating velocity was slowed-down to increase column efficiency by 100 %. Then, the performances of a square cross-section capillary column were compared with those of a conventional circular cross-section column. The coating efficiencies were estimated at 55 % and 42 % for circular and square internal cross-section geometries respectively, demonstrating that the square section geometry did not affect significantly the column performances. Stationary phase films of different thicknesses, from 0.032 to 0.260 µm, were coated on different microchips of the same production batch to assess the influence of film thickness on the chromatographic performances. Microchips performance was evaluated through Golay's plots, and some of the microchips were also studied by optical microscopy. The efficiency and retention capacity of our microchip column is shown to be highly dependent on the film thickness (9,000 plates.m-1 with k = 0.12 for the thinnest film and up to almost 4,000 plates.m-1 with k = 0.93 for the thickest film). Finally, the coating process repeatability was validated by producing three identical microchips with RSD of 4.8 % for the average number of theoretical plates.

Digital twin-based multi-granularity synchronisation for production-warehousing under batch processing mode

Batch production is a cost-effective way that boosts efficiency in manufacturing systems. As customer demands become increasingly customised, there is a growing need to consolidate numerous small-batch orders. However, if batch production exceeds the optimal level, it may lead to the premature completion of customer orders, causing temporary congestion in the finished goods warehouse and prolonged resource occupation. Consequently, an essential focus of this study pertains to coordinating decision-making units like workshops and warehouses within batch production setups to ensure stable system operation amidst dynamic customer demands. The proposed solution encompasses a Digital Twin-based Multi-level Synchronised Control System (DTMCS) framework, featuring the Multi-granularity Synchronised Control Mechanism (MgSCM) and a collaborative optimisation model for batch production and warehouse planning. By examining a practical case study from a paint manufacturing company, the research delves into the effects of four synchronised control patterns on various system performance metrics, such as operating costs, equipment and warehouse utilisation rates, and system stability. Overall, this study provides an adaptable and cost-effective solution for enhancing production-logistics synchronisation.

A scalable Deep Q-Learning approach for hot stamping process under dynamic control environment

Despite the significant advancements in Artificial Intelligence and its applications, traditional control techniques still dominate industrial and manufacturing processes. These techniques often rely on static setups and default values, which make them resistant to environmental changes. However, in dynamic and uncertain contexts, real-time process adaptation offers significant advantages in decision-making and control. In this research, we present a novel approach utilising Deep Reinforcement Learning for real-time dynamic parameter adaptation and autonomous control of processes in unpredictable environments. The effectiveness of our proposal is illustrated through the development and training of a Deep Q-Learning control system tailored for optimising a hot stamping process. The control model aims to minimise defect rates and enhance efficiency by reducing forming times during batch productions. Comparative analysis against conventional industry practices, characterised by static and non-adaptive time settings, validates the efficacy of our solution, with improvements of over 20% in the times, depending on the scenario considered. Experimental evaluation in a semi-industrial pilot setting not only demonstrates the adaptability of our approach but also highlights its potential scalability for diverse industrial applications.

A method and tool for Sustainability-driven Computer-Aided Process Planning with user-tuneable optimization

This paper introduces the methodology framework, and a cloud-based Sustainability-driven Computer-Aided Process Planning (s-CAPP) tool aimed at optimizing efficiency and sustainability aspects of manufacturing operations at shopfloor level, considering a user-tailored optimization goal function. Geometric data extracted from 3D models of the parts to be produced are coupled with process parameters and resource consumption data from the manufacturing equipment in order to analyze the metrics for productivity and sustainability accordingly. By leveraging comprehensive data resources in manufacturing, s-CAPP allows companies to balance their production plans between productivity and sustainability through a user-friendly interface. Its application in a furniture manufacturing case study demonstrates its potential for process planning by prioritizing between productivity or sustainability, showcasing promising results. The presented s-CAPP implementation assists the production managers in taking decisions regarding sustainability and productivity in the production of batches through the prompting of intuitive visual results, impacting on both economic and environmental sides. Keywords: sustainability-driven manufacturing, process planning, cloud-based computing, furniture manufacturing, process simulation, data-driven manufacturing, feature extraction

Dynamic modeling and real-time performance analysis of multi-product batch manufacturing systems with perishable products

Production lines handling perishable goods in industries such as petrochemical and food processing demand meticulous precision in timing. These systems are governed by stringent timeframes, where products must strictly adhere to defined residence times. Exceeding these time constraints leads to product scrapping, posing a significant threat to overall productivity. Modeling such intricate systems presents a formidable challenge, and success hinges upon a deep understanding of the complex interplay between production and batch scrapping dynamics. In response to this challenge, this paper presents a novel model for a multiproduct batch production line with perishable products. Furthermore, the real-time performance metric of Permanent Production Loss (PPL) is extended to this system using a method of PPL back-adjustment in time. To validate the fidelity of the proposed mathematical model, numerical case studies are conducted, comparing the developed model to a discrete event simulation. The results demonstrate that the model accurately represents the system at hand. The expressions for PPL evaluation and attribution are also validated using case studies.

Integrated batch production planning and scheduling with machine-learning based production capacity region considering wind energy system

Continued greenhouse gas emission has led to increased global warming. Towards the goal of carbon emissions reduction and environmental sustainability, replacing high-polluting fossil fuel by clean energy has become a top priority in the decision-making of the current chemical industry. This work presents an optimization framework to address the integration of batch chemical production planning and scheduling considering energy consumption, which is a vital operational problem in chemical production. Assisting the linkage between planning layer and scheduling layer, a novel linear support vector machine based production capacity region calculation method is proposed to identify feasible regions of the scheduling layer. Uncertain factors in both the production system and the energy system are considered, including demand volatility of chemical products, energy supply fluctuations and time-varying energy market prices. To evaluate each strategy and the proposed model, simulation experiments are performed in three representative case studies. The results reveal that the integrated model considering energy consumption shows superior performance in different energy configurations, with or without wind energy generation and battery storage systems.