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Cellulose was isolated from recycled pulp and paper sludge and used to synthesize cellulose nanocrystals. Response surface methodology and Box-Behnken design model were used to predict, improve, and optimize the cellulose isolation process. The optimal conditions were a reaction temperature of 87.5 °C, 180 min with 4% sodium hydroxide. SEM and TEM results revealed that the isolated cellulose had long rod-like structures of different dimensions than CNCs with short rod-like structures. The crystallinity index from XRD significantly increased from 41.33%, 63.7%, and 75.6% for Kimberly mill pulp sludge (KMRPPS), chemically purified cellulose and cellulose nanocrystals, respectively. The TGA/DTG analysis showed that the isolated cellulosic materials possessed higher thermal stability. FTIR analysis suggested that the chemical structures of cellulose and CNCs were modified by chemical treatment. The cellulose surface was highly hydrophilic compared to the CNCs based on the high water holding capacity of 65.31 ± 0.98% and 83.14 ± 1.22%, respectively. The synthesized cellulosic materials portrayed excellent properties for high-end industrial applications like biomedical engineering, advanced materials, nanotechnology, sustainable packaging, personal care products, environmental remediation, additive manufacturing, etc. [ABSTRACT FROM AUTHOR]

The global advocacy of resource conservation and waste management emphasizes the significance of sustainable practices, particularly in sectors such as paper manufacturing and recycling. Currently, conventional chemical methods are predominant for paper production, necessitating the use of substantial amount of toxic chemicals. This chemical-intensive approach compromises the recycled fiber quality, generates hazardous effluent causing serious ecological threats which triggers regulatory complexities for the mills. To address these challenges modern research suggests adopting sustainable eco-friendly practices such as employing enzymes. This review aims to explore the applicability of ‘laccase’ enzyme for paper recycling, investigating its properties and contribution to improved recycling practices. By delving into the potential application of laccase integration into the papermaking process, this article sheds light on the limitations inherent in traditional methods surmounted within both research and translational landscapes. Culture and process optimization studies, supporting the technological improvements and the future prospects have been documented. [ABSTRACT FROM AUTHOR]

Currently, tissue product producers try to meet consumers' requirements to retain their loyalty. In perforated products, such as toilet paper, these requirements involve the paper being portioned along the perforation line and not outside of it. Thus, it becomes necessary to enhance the behavior of the perforation line in perforated tissue papers. The current study aimed to verify if the perforation line for 0° (the solution found in commercial perforated products) is the best solution to maximize the perforation efficiency. A finite element (FE) simulation was used to validate the experimental data, where the deviations from the experiments were 5.2% for the case with a 4 mm perforation length and 8.8% for a perforation of 2 mm, and optimize the perforation efficiency using the genetic algorithm while considering two different cases. In the first case, the blank distance and the perforation line angle were varied, with the best configuration being achieved with a blank distance of 0.1 mm and an inclination angle of 0.56°. For the second case, the blank distance was fixed to 1.0 mm and the only variable to be optimized was the inclination angle of the perforation line. It was found that the best angle inclination was 0.67°. In both cases, it was verified that a slight inclination in the perforation line will favor partitioning and therefore the perforation efficiency. [ABSTRACT FROM AUTHOR]

The integrated industrial control systems require a reliable communication among each part of control systems. The limitation of network topology comprises the low latency of data transmission. The network algorithm topologies are sometimes failed to ensure the faulttolerant network design even if they offer it. Therefore, this works investigates a Node Insertion Algorithm (NIA) and Ant Colony Optimization (ACO) in order to solve the Travelling Salesman Problem (TSP) for network topologies in a communication network. The NP Non deterministic (polynomial time)-hardness based of TSP leads to the impossibility of polynomial time algorithm to solve an exponentially increasing amount of communication addresses. TSP itself is a well-known solution method known in the graph theory which is based on approximation algorithm. Therefore, this work also presents a comparison between the NIA and ACO for 200 nodes of network topologies. The results show that NIA is superior than ACO in terms of resulting ring's cost and computation duration. Three topologies are tested and it is observable that NIA requires only 14.88 seconds to finish the computation in which the resulting ring's length reaches 164019 units. Therefore, the achievement of the NIA can be applied for logical and physical nodes for a reliable connection in an industrial topology network. [ABSTRACT FROM AUTHOR]

[Display omitted] • Optimization study of Photo-Fenton and PDS/UV processes for BOD and COD removal. • Photo-Fenton process was able to enhance biodegradability. • PDS/UV process on the best condition only could maintain the biodegradability. • The operational cost of Photo-Fenton processes is much cheaper than PDS/UV process. The aim of this study is to provide an Advanced Oxidation Process (AOP) based solution to enhance biological treatment efficiency by improving biodegradability of wastewater. Photo-Fenton and Peroxydisulfate (PDS)/UV processes were selected in this study as a representative of hydroxyl and sulfate radical based AOPs. A central composition design (CCD) employed to optimize operational cost and BOD/COD ratio to determine optimal conditions for enhancing wastewater treatment efficiency. The study achieved a maximum COD removal of 71.5 % and increased wastewater biodegradability. Optimal conditions for Photo-Fenton process were DOD (H 2 O 2) 0.28, [Fe2+] 106.26 mg/L, and pH 2.32, costing 2.27 USD per 1 kg COD removed. For PDS/UV process, 75.5 % COD removal was achieved at a cost of 10.6 USD/1 kg removed COD. The PDS/UV process had higher COD removal than Photo-Fenton but did not improve wastewater biodegradability. Optimal conditions were DOD (PDS) 0.19, pH 6.68, costing 10.6 USD/1 kg COD removed. [ABSTRACT FROM AUTHOR]

The air-drying of resin impregnated paper sheets in industrial lines, formed by a serial array of furnaces, presents a high number of different controllable operational parameters whose adjustment, usually done by the maintenance staff, leads to non-efficient configurations. A model-based numerical tool, which predicts accurately in a few seconds the evolution of the paper temperature and paper grammage along the line for a given combination of the input operational parameters (direct design), was used coupled to an optimization tool to select appropriate operational parameters (inverse design) that ensure a drying process quality (i.e., fulfills an objective grammage profile) with a minimum of energy consumption. The numerical tool was capable of selecting suitable configurations with an energy reduction of up to 50% for several tested industrial cases, making the model an essential tool in the framework of the increasingly relevant role of digital twins in industry. [ABSTRACT FROM AUTHOR]

Banana stem is a known alternative to wood for the production of pulp and paper. During the production of paper, it is extremely important to save costs and time while at the same time enhancing quality of produced paper. In the present study, paper was developed from banana stem fiber by optimizing its density. Response surface methodology (RSM) using Box-Behnken design (BBD) was used to determine optimum blending parameters of the developed papers. The influence of banana stem fiber amount (250-1000 g), water amount (1-2liters) and blending time (5-10 min) on paper density was ana)lyzed using software Stat-Ease 360, Version 13. The optimum conditions of banana stem fiber amount, water amount and blending time to achieve a density of 675.75 g/m3 were determined as 461.83 g, 1.31liters, and 8.53 min, respectively. ANOVA results revealed that the quadratic model best fits the density response, with blending time and banana stem fiber amount as the most significant model terms. Linear effects were more predominant than quadratic and interaction effects. The developed density model was in close agreement with experimental findings with an R² value of 0.9748. Developed paper had cellulosic contents, with volatiles as high as 82.11%wb and 2.11% wb ash composition. Future works on thermal properties of paper are encouraged. [ABSTRACT FROM AUTHOR]

• A Goal Programming model to optimize a waste paper recycling network was elaborated. • The model considers objectives related to sustainability, multiple products and periods. • Vehicle routing, different-capacity vehicles and multiple trips are highlights. • The possibility of storing material at the collection points is considered. • The formulation provides relevant solutions to assist decision-makers. Organization and planning of reverse logistics networks make sustainable processes more efficient. Thus, an important sector for connecting the collection and waste paper recycling echelons is the intermediate center. In this study, a Mixed Integer Linear Programming model, which is multi-objective, multi-product, multi-level and multi-period, was developed to optimize the waste paper logistics processes of intermediate centers. The formulation includes the following echelons: collection, considering vehicle routing with different capacities; inventory of non-baling materials; baling sorted waste and inventory of bales; selling bales; disposal of non-recyclable waste. The aims of the model include: maximizing the collected waste; minimizing distances; maximizing both the production and sale of bales, and minimizing costs. According to research carried out in the literature, the developed model is a new proposal and to implement it, the Weighted Goal Programming and Revised Multi-Choice Goal Programming approaches were used to deal with multiple objectives and incorporate uncertainty into the quantity of waste available for collection. To analyze the proposed model, computational tests were executed with instances based on real data from a Brazilian company in the sector. For all performed tests, General Algebraic Modeling System 23.6.5 modeling language and CPLEX 12.2.0.2 solver were used for modeling and optimization. The results show that this study presents formulation and technological approaches that represent real situations and provide competitive solutions to the problem. [ABSTRACT FROM AUTHOR]

Background and Objectives: Wastewater treatment of paper recycling and cardboard industries with the aim of water recycling and reducing environmental impact is one of the daily needs of industries. Therefore, the aim of the present study was to optimize the conditions of coagulation and flocculation process in wastewater treatment of paper recycling industries using the response surface method. Materials and Methods: In this laboratory study, response surface method and central composite design were used to design the experiments and to determine the effects of the studied variables (pH, concentrations of alum, Poly aluminum chloride (PAC), and cationic polymer) on the total suspended solid (TSS) and chemical oxygen demand (COD) removal efficiency. Data analysis was done using ANOVA statistical test with a significant level of 0.05. Results: The results showed that in the combination of PAC with cationic polymer (optimal conditions; pH=7.5, PAC concentration=450 mg/L, cationic polymer concentration=1.5 mg/L), the TSS and COD removal efficiency were 97.34% and 75.76%, respectively. In combination of alum with cationic polymer (optimal conditions; pH=8.5, alum concentration=550 mg/L, cationic polymer concentration=2 mg/L), removal efficiencies were 98.96% and 77.83%, respectively. The simultaneous effects of two independent variables, pH and concentration, had a significant effect (p<0.05) in removing TSS and COD variables. Conclusion: The results showed that the combination of PAC with cationic polymer had a better efficiency. Also, the COD and TSS in the final effluent were higher and lower than the standard values, respectively. Therefore, chemical coagulation process is proposed as pretreatment. [ABSTRACT FROM AUTHOR]

This work reports the degradation of cellulosic material, i.e., paper, under different environmental conditions (soil and hydrolytic). Models were developed using central composite design (CCD) within the framework of response surface methodology (RSM) and artificial neural network (ANN) techniques. It is done as part of a solid waste management system to eliminate the waste produced by excessive paper usage and obtain optimized degradation conditions of paper. In view of the real environments where paper-based solid wastes are usually exposed, soil degradation and hydrolytic degradation of paper were investigated. The factors pH (4–10), compost ratio (2–5), CaCl2 (5–15 ppm), and time (7–20 days) were independent factors that varied for the study, and degradation conversion was measured as the dependent variable for soil degradation of paper. The factors pH (4–10), CaCl2 (2–5 ppm), tripotassium phosphate (TPP, 2–5 ppm), and time (6–16 days) were independent variables that varied for the study, and degradation conversion was the dependent variable for hydrolytic degradation of paper. The optimal conditions for soil degradation were determined to be a pH of 4, a compost ratio of 5, a salt (CaCl2) addition of 5 ppm, and a duration of 20 days, resulting in the highest observed conversion rate. The coefficient of regression (R2) for CCD is 87.24%, whereas it is 94.82% for ANN. The optimal conditions for achieving maximum conversion in hydrolytic degradation include a pH of 10, a CaCl2 salt concentration of 5 ppm, a TPP salt concentration of 2 ppm, and a duration of 16 days. The coefficient of regression for CCD is 75.15%, while the coefficient of regression for ANN is 93.91%. In both deterioration scenarios, the data exhibited a higher degree of fit when modeled using ANN compared to CCD (RSM). [ABSTRACT FROM AUTHOR]

Summary: This article proposes a methodology based on artificial intelligence to enhance methane production from waste paper. The proposed methodology combines fuzzy logic‐based modelling and modern optimization. Firstly, a robust Adaptive Network‐based Fuzzy Inference System model of methane production process through fuzzy logic modelling is created using experimental datasets. Second, a particle swarm optimizer was used to obtain the optimal process conditions. During the optimization procedure, the beating time and feedstock/inoculum ratio are employed as decision variables in order to maximize methane production. The obtained resulted from the proposed methodology are compared with those obtained by response surface methodology. The results of the comparison confirmed the superiority of the proposed methodology. The fuzzy model shows a better fitting to the experimental data compared to ANOVA. The fuzzy model showed a higher coefficient of determination and a lower value of root mean squared errors compared to ANOVA. Moreover, the proposed strategy, that is, modelling and optimization, is an effective method for increasing the biomethane yield at extended range conditions. [ABSTRACT FROM AUTHOR]

Paper mills are among the most polluting industries, responsible for many organic and inorganic compounds emissions. The fibres electro-kinetic features strongly affect the ability to retain fillers since the fillers–fibres interactions are charge induced. The control and the prediction of these parameters would represent a precious aid for process management, allowing the fillers retention enhancement, a lower environmental impact and the paper sheet properties streamlining. The work presented deals with the implementation and training of four artificial neural networks (ANNs) for the prediction of the main electrochemical and physical features of cellulose pulp and paper. First, two ANNs predict the electrochemical parameters. Following, they were applied to predict the paper sheet properties and fillers retention. The neural models implemented showed outstanding prediction performance, with R2 in the order of 0.999 and a low mean error. The results demonstrate how Artificial Neural Networks may be a valuable instrument for paper mill pollutant reduction. However, they suggest a more inclusive investigation for a better fibres behaviour representation. • Main process parameters of an industrial papermaking process were identified. • Experimental datasets were achieved during industrial production. • Artificial Neural Networks were trained for process parameters prediction. • Accurate predictions of papermaking process were obtained. [ABSTRACT FROM AUTHOR]

TiO 2 /PS composite is evaluated in photodegradation of lignin under UVA-LED irradiation. • TiO 2 /PS composite was synthesized by solvent-casting method. • The effects of operational parameters were evaluated and optimized by Box-Behnken design. • The toxicity effects after photodegradation process were not observed. • The reusability study confirmed the stability of TiO 2 /PS composite. In the present study, the photodegradation of lignin was studied using TiO 2 /Polystyrene composite under UV-LED irradiation in a batch-recirculated photoreactor as a supplementary treatment for pulp and paper industries. TiO 2 /PS composite was synthesized by the solvent-casting method and was characterized by scanning electron microscopy (SEM) and EDX analysis. The 3-level, 3-factor Box-Behnken design was applied to study the effects of main operating parameters and optimization of process circumstances. Besides, COD reduction efficiency, toxicity assays, and reusability were also investigated. The predicted model had R2 and R2 adj correlation coefficients of 0.97 and 0.99 respectively, which showed experimental results were very close to the predicted values. Furthermore, under the optimal conditions (reaction time = 72.5, volumetric flow rate = 230 mL/min and lignin = 51.8 mg/L) the lignin degradation efficiency was 93.98%. The maximum COD removal (89%) obtained by 250 mL/min volumetric flow rate and lignin concentration of 50 mg/L after 120 min reaction time. Bioassay using D. Magna exhibited that at the beginning of the process, the toxicity of solution increased but continuously decreasing after then to very low values achieved within 120 min of photodegradation. In addition, the reusability tests demonstrated that the TiO 2 /PS composite after 5 cycles has a good stability and photocatalytic activity. [ABSTRACT FROM AUTHOR]

Abstract The global demand for bio-plastic particularly polyhydroxyalkanoate (PHA) have been increased in the last few decades as a substitute of petrochemical-based plastic. Utilization of waste paper, the primary constituent of municipal solid waste (MSW), as a carbon source for polyhydroxybutyrate (PHB) production is not only an alternative, environmental friendly route of waste management but also helps to valorize the waste. In this study, hydrogen peroxide pretreated waste paper saccharification has been optimized using central composite design (CCD). The maximum hydrolysis (88.18%) occurred at paper loading 5.0 g/L, agitation 242 rpm, working volume 20%, cellulase 49.82 U/g, β-glucosidase 20.9 U/g and hemicellulase 29.5 U/g. PHB synthesis and biomass accumulation by xylose-utilizing Burkholderia sacchari using waste paper hydrolysate were studied using different nitrogen sources and carbon to nitrogen (C/N) ratios. Maximum PHB and dry cell weight (DCW) occurred with ammonium sulfate and a C/N ratio of 20. The highest biomass (3.63 g/L), the maximum PHB accumulation (44.2%) and the maximum reducing sugar utilization (92.1%) were observed after 48 h of cultivation using diluted hydrolysate. The physicochemical properties of the extracted PHB were compatible with the standard PHB. Hence, the waste paper could be exploited as a renewable feedstock for the sustainable production of PHB. Graphical abstract Image 1 Highlights • The maximum enzymatic hydrolysis 88.18% was achieved by response surface method. • PHB produced from waste paper hydrolysate using xylose utilizing B. sacchari. • Maximum PHB and dry cell weight occurred with (NH 4) 2 SO 4 and C/N ratio of 20. • High thermostability of PHB with a maximum degradation rate around ∼ 300 °C. • PHB production using waste paper is cost effective and ecofriendy approach. [ABSTRACT FROM AUTHOR]

Laccase is a versatile enzyme that plays a major role in the remediation of various environmental pollutants. In this work, a thermo-tolerant halophilic Bacillus aquimaris AKRC02 was isolated from pulp and paper mill waste sludge for efficient laccase production. Various agro-industrial waste residues, including potato peel, banana peel, sawdust, pea peel, wheat bran, orange peel, and rice bran, were screened to produce laccase using a submerged fermentation process. Among these, rice bran supported the maximum laccase production (4.58 U/mL). The optimized environmental conditions (incubation time 120 h; 4.58 U/mL), 35 0C; 6.624 U/mL) and pH 7.0; 10.142 U/mL) and nutritional sources (glucose 1.0%; 14.164 U/mL and peptone 0.5%; 18.124 U/mL) significantly enhanced the laccase production. Purified laccase showed a specific activity and purification fold of 228.34 U/mg and 38.08, respectively. The purified enzyme showed a molecular weight of 65 kDa and high thermal stability at 45 0C for 8 h. In conclusion, the remarkable properties of the newly isolated bacterium may provide a significant opportunity for degrading environmental contaminants, making it an attractive biocatalyst for industrial applications. [ABSTRACT FROM AUTHOR]

The aim of this paper is the development of a mathematical model to compute the optimal inventory mix to face stochastic demand at minimum cost in a two-level supply chain. The paper addresses a multi-product dynamic lot-sizing problem under stochastic demand subject to capacity and service level constraints. This model is executed to compare a Make To Order (MTO) strategy to a Vendor Managed Inventory (VMI) partnership between the supplier and their customers. Both strategies provide the demand order to be produced. A schedule of production orders is determined over the planning horizon in order to minimize the inventory holding costs of the supply chain, taking into consideration that the supplier is also responsible of initiating the replenishment orders and deliveries of their customers according to the VMI partnership. The simulation model is illustrated empirically using a real case study: a paper manufacturing company that pursues to improve customer service level and supply chain inventory costs through a proper production planning of their paper machines and a suitable VMI order replenishment schedule. [ABSTRACT FROM AUTHOR]

[Display omitted] • Waste office paper was utilized as a substrate for microbial lipid production. • A detailed optimization study has been conducted for biomass and lipid yield. • Optimization results in10.55 g/L biomass, 6.13 g/L lipid with 58% lipid content. • Biodiesel properties of the produced lipids met with the international standards. • A solution to mitigate the hazardous effects caused by waste and fossil fuel. Environmental issues caused by fossil fuel and its rapid depletion demand the need to produce biofuel from renewable energy resources. The disposal of waste paper in landfills causes air pollution and contaminates groundwater. Hence, exploiting paper waste as a renewable resource for microbial lipid production can solve these environmental issues in a greener way. Further, these microbial lipids are a reliable source for biodiesel production. In this study, waste office paper was utilized as a substrate to produce microbial lipids through fermentation by the oleaginous yeast, Rhodosporidium toruloides, using a biorefinery approach. The production conditions were optimized using the Box-Behnken design; a fermentation time of 116.16 h, a C/N ratio of 60, and an inoculum concentration of 9.67% yielded a biomass of 10.55 g/L and a lipid yield of 6.13 g/L with a total lipid content of 58%. The major lipids produced were oleic acid (42%), palmitic acid (36%), stearic acid (9.8%), and myristic acid (8%), which clearly indicates the suitability of the lipids produced by R. toruloides for biodiesel production. The predicted biodiesel properties of the transesterified product met the international standard specifications. Hence, utilizing waste office paper for biodiesel production will be an effective way of mitigating the environmental pollution caused by waste paper and the harvesting of fossil fuels. [ABSTRACT FROM AUTHOR]

Although microfluidic paper-based analytical devices (μPADs) get a lot of attention in the scientific literature, they rarely reach the level of commercialization. One possible reason for this is a lack of application of machine learning techniques supporting the design, optimization and fabrication of such devices. This work demonstrates the potential of two chemometric techniques including design of experiments (DoE) and digital image processing to support the production of μPADs. On the example of a simple colorimetric assay for isoniazid relying on the protonation equilibrium of methyl orange, the experimental conditions were optimized using a D-optimal design (DO) and the impact of multiple factors on the μPAD response was investigated. In addition, this work demonstrates the impact of automatic image processing on accelerating color value analysis and on minimizing errors caused by manual detection area selection. The employed algorithm is based on morphological recognition and allows the analysis of RGB (red, green, and blue) values in a repeatable way. In our belief, DoE and digital image processing methodologies are keys to overcome some of the remaining weaknesses in μPAD development to facilitate their future market entry. Image 1 • Optimization of device geometry and amount of assay reagent using D-optimal design. • Investigation of seven factors at three levels by only performing 44 trials. • MATLAB-based morphological recognition algorithm for signal readout. [ABSTRACT FROM AUTHOR]

This paper investigates the possibility of combining different forest industries (a pulp and paper mill, its supply chain, and a wood-pellet plant) into an integrated industrial site in which they share a common heat and power utility. Advanced process integration and optimization techniques are used to study the site from both material and energy viewpoints. An existing pulp and paper mill is used as the site core plant and its pulp and paper production rates are kept fixed as they are in reality, while the other material flow links among the plants are based on the current industrial situation in Sweden. Different scenarios are evaluated in order to reflect the two main objectives that can be pursued (increased electricity production or biomass resource saving) and the two technologies that can be considered for the shared CHP system (boilers and product gas fired gas turbines). The corresponding non-integrated (standalone) configurations are compared to these scenarios to quantify the potential benefits of the integration. Investment opportunity is also calculated for the considered scenarios as an indicator of the economic convenience. [ABSTRACT FROM AUTHOR]

Executive Summary: The EC Directive 2013/59/Euratom states in article 56 that exposures of target volumes in nuclear medicine treatments shall be individually planned and their delivery appropriately verified. The Directive also mentions that medical physics experts should always be appropriately involved in those treatments. Although it is obvious that, in nuclear medicine practice, every nuclear medicine physician and physicist should follow national rules and legislation, the EANM considered it necessary to provide guidance on how to interpret the Directive statements for nuclear medicine treatments. For this purpose, the EANM proposes to distinguish three levels in compliance to the optimization principle in the directive, inspired by the indication of levels in prescribing, recording and reporting of absorbed doses after radiotherapy defined by the International Commission on Radiation Units and Measurements (ICRU): Most nuclear medicine treatments currently applied in Europe are standardized. The minimum requirement for those treatments is ICRU level 1 ("activity-based prescription and patient-averaged dosimetry"), which is defined by administering the activity within 10% of the intended activity, typically according to the package insert or to the respective EANM guidelines, followed by verification of the therapy delivery, if applicable. Non-standardized treatments are essentially those in developmental phase or approved radiopharmaceuticals being used off-label with significantly (> 25% more than in the label) higher activities. These treatments should comply with ICRU level 2 ("activity-based prescription and patient-specific dosimetry"), which implies recording and reporting of the absorbed dose to organs at risk and optionally the absorbed dose to treatment regions. The EANM strongly encourages to foster research that eventually leads to treatment planning according to ICRU level 3 ("dosimetry-guided patient-specific prescription and verification"), whenever possible and relevant. Evidence for superiority of therapy prescription on basis of patient-specific dosimetry has not been obtained. However, the authors believe that a better understanding of therapy dosimetry, i.e. how much and where the energy is delivered, and radiobiology, i.e. radiation-related processes in tissues, are keys to the long-term improvement of our treatments. [ABSTRACT FROM AUTHOR]

A pilot-scale aerated lagoon was used for the aerobic treatment of pulp and paper mill effluent from September 1 to December 1, 2014. The aerated lagoon was installed at the chemistry laboratory in the Chemical Engineering Department at the Durango Institute of Technology and was fed with real pulp and paper mill effluent. The experimental work was run under various operating conditions. The operating parameters (total and volatile suspended solids (VSS) and dissolved oxygen concentration (DO)) and environmental variables (temperature, pH, COD, and BOD5 of influent water) were monitored daily. In all the experiments conducted, the aerated lagoon generated an effluent of optimal quality complying with the requirements of SEMARNAT (2003) and CONAGUA (2003). A model that explains the behavior of the system under realistic operating conditions was obtained. The model indicated an optimal DO of approximately 4 mg/L for concentrations up to 1000 mg/L, showing variations in concentrations above this value. This data indicate that the flexibility of the bacterial formulation and its ability to adapt to environmental changes play an important role in the stability of an aerated lagoon. [ABSTRACT FROM AUTHOR]

Highlights • First report on cellulase production by Bacillus velezensis using waste paper. • Pretreatment increased the cellulose content without any inhibitors formation. • Central composite design increased the cellulase production by three fold. • Cost-effective approach of waste management is achieved. Abstract This paper reports the cellulase (FPase) production by newly isolated Bacillus velezensis ASN1 using waste office paper (WOP) as feedstock and optimization of production conditions through two level factorial design, steepest ascent/descent and second order response surface methodology (RSM). Various fermentation parameters, like chemical factors (potassium dihydrogen phosphate, potassium chloride, yeast extract, magnesium sulphate, sodium nitrate, Tween 80, and waste office paper), physical factors (temperature, pH and time) and biological factor (inoculum size) were examined using two level full factorial design to check the key factors significantly affecting the cellulase production. The central composite design (CCD) was used to optimize the vital fermentation parameters, such as carbon (WOP), nitrogen, pH, and inoculum concentration in the medium for achieving higher cellulase production. The optimum medium composition was found to be WOP (9 g/L), sodium nitrate (0.35 g/L), inoculum size (6.56%) and pH 4.72. The model prediction of 2.46 U/mL cellulase activity at optimum conditions was verified experimentally as 2.42 U/mL. [ABSTRACT FROM AUTHOR]

[Objective] The present paper aimed to get high efficient cellase-producing bacteria and determine the taxonomy. [Method] Sodium carboxymethylcellulose was selected as the sole carbon source to isolate cellulose-producing microorganism. A total of 51 bacteria and actinomyces were isolated from natural fermentation material of tobacco straw.Nine isolates possessed high cellulase activities in congo dye experiment. The phylogenetic relationships of cellulase producing bacteria and actinomyces were determined through the analyses of phylogenetic trees constructed using 16 S rRNA gene sequences. [Result] Luteibacter sp.L43 possessed the highest cellulase activities in enzyme activity tests. The effects of the single factor including pH, carbon and nitrogen concentration on filter paper enzyme, endo-β-1, 4-glucanases and exo-β-1, 4-glucanases for L43 were determined.Under the condition of pH6, sodium carboxymethylcellulose concentration 12.5 g/L and NaNO3 concentration 5 g/L, the highest cellulase activities of endo-β-1, 4-glucanases and exo-β-1, 4-glucanases of L43 were 16.9 and39.62 U/mL, respectively. [Conclusion] This study provided a good cellulose producing bacteria for tobacco straw fermentation and laid a foundation for the development and utilization L43. [ABSTRACT FROM AUTHOR]

To address the growing need for economically viable sludge processing technologies and associated resource recovery, a novel convective high-velocity pilot cyclone was set-up at the Swedish University of Agricultural Sciences in Umeå, Sweden. In essence this process entails feeding (⩽900 kg h −1 ) a material to a heated high-velocity air flow (approx. 13 × 10 3 m 3 h −1 ) allowing moisture removal at low temperatures. This equipment is expected to improve drying of challenging industrial sludge materials when used as a pretreatment method prior to further processing. Modeling results based on an experimental campaign on recycled paper mill sludge indicated that efficient drying with a specific energy consumption of ⩽1–1.2 kWh kg −1 H 2 O can be achieved with inlet air temperature levels ⩾40 °C coupled with respective feeding capacity of ⩽750 kg h −1. [ABSTRACT FROM AUTHOR]

In the present study, the photodegradation of lignin was studied using TiO2/Polystyrene composite under UV-LED irradiation in a batch-recirculated photoreactor as a supplementary treatment for pulp and paper industries. TiO2/PS composite was synthesized by the solvent-casting method and was characterized by scanning electron microscopy (SEM) and EDX analysis. The 3-level, 3-factor Box-Behnken design was applied to study the effects of main operating parameters and optimization of process circumstances. Besides, COD reduction efficiency, toxicity assays, and reusability were also investigated. The predicted model had R² and R²adj correlation coefficients of 0.97 and 0.99 respectively, which showed experimental results were very close to the predicted values. Furthermore, under the optimal conditions (reaction time = 72.5, volumetric flow rate = 230 mL/min and lignin = 51.8 mg/L) the lignin degradation efficiency was 93.98%. The maximum COD removal (89%) obtained by 250 mL/min volumetric flow rate and lignin concentration of 50 mg/L after 120 min reaction time. Bioassay using D. Magna exhibited that at the beginning of the process, the toxicity of solution increased but continuously decreasing after then to very low values achieved within 120 min of photodegradation. In addition, the reusability tests demonstrated that the TiCh/PS composite after 5 cycles has a good stability and photocatalytic activity. [ABSTRACT FROM AUTHOR]

Owing to increasing competition, the marketing of commercial software products is a challenging task. Diverse marketing strategies are adopted to tap the huge market share. Among these, pricing and warranty are the most significant ones. The warranty policies organizations adopt usually have two dimensions: duration and effort spent during the warranty phase. In this paper, two variables are broadly taken: testing, which is an essential phase during software development, and warranty, which is a crucial component of after‐sales service. Here, we will formulate an optimization problem where returns and expenses will be influenced by price, testing resources spent, the duration for which testing is done, warranty duration and effort spent during warranty for the software product. The product sales are assumed to follow a Stochastic Bass model based on Non‐Homogeneous Poisson Process (NHPP). A free on‐site support warranty with an upper limit on duration and effort covers the 2D failure process. The numerical illustration is presented wherein profit is maximized by conjointly optimizing five variables, that is price, testing duration, testing effort, warranty duration and warranty effort. This study emphasizes on application of a popular soft computing, metaheuristic technique named genetic algorithm (GA) to solve an optimization problem for a software system. The solution to the Optimization problem is obtained using the GA tool in MATLAB. The results obtained have relevance in the industry for finding optimal values of significant variables before the release of newly developed software products to the ultimate client. The concept discussed in this paper has diverse applications in computer engineering, software and hardware industry. [ABSTRACT FROM AUTHOR]

A Virtual Power Plant (VPP) is a centralized energy system that manages, and coordinates distributed energy resources, integrating them into a unified entity. While the physical assets may be dispersed across various locations, the VPP integrates them into a virtual unified entity capable of responding to grid demands and market signals. This paper presents a tri-level hierarchical coordinated operational framework of VPP. Firstly, an Improved Pelican Optimization Algorithm (IPOA) is introduced to optimally schedule Distributed Energy Resources (DERs) within the VPP, resulting in a significant reduction in generation costs. Comparative analysis against conventional algorithms such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) demonstrates IPOA's superior performance, achieving an average reduction of 8.5% in generation costs across various case studies. The second stage focuses on securing the optimized generation data from rising cyber threats, employing the capabilities of machine learning, preferably, a convolutional autoencoder to learn the normal patterns of the optimized data to detect deviations from the optimized generation data to prevent suboptimal decisions. The model exhibits exceptional performance in detecting manipulated data, with a False Positive Rate (FPR) of 1.92% and a Detection Accuracy (DA) of 98.06%, outperforming traditional detection techniques. Lastly, the paper delves into the dynamic nature of the day ahead market that the VPP participates in. In responding to the grid by selling its optimized generated power via the day-ahead market, the VPP employs the Prophet model, another machine learning technique to forecast the spot market price for the day-ahead to mitigate the adverse effects of price volatility. By utilizing Prophet forecasts, the VPP achieves an average revenue increase of 15.3% compared to scenarios without price prediction, emphasizing the critical role of predictive analytics in optimizing economic gains. This tri-level coordinated approach adopted addresses key challenges in the energy sector, facilitating progress towards achieving universal access to clean and affordable energy. [ABSTRACT FROM AUTHOR]

Background: Transcranial electrical stimulation (tES) generates an electric field (or current density) in the brain through surface electrodes attached to the scalp. Clinical significance has been demonstrated, although with moderate and heterogeneous results partly due to a lack of control of the delivered electric currents. In the last decade, computational electric field analysis has allowed the estimation and optimization of the electric field using accurate anatomical head models. This review examines recent tES computational studies, providing a comprehensive background on the technical aspects of adopting computational electric field analysis as a standardized procedure in medical applications. Methods: Specific search strategies were designed to retrieve papers from the Web of Science database. The papers were initially screened based on the soundness of the title and abstract and then on their full contents, resulting in a total of 57 studies. Results: Recent trends were identified in individual- and population-level analysis of the electric field, including head models from non-neurotypical individuals. Advanced optimization techniques that allow a high degree of control with the required focality and direction of the electric field were also summarized. There is also growing evidence of a correlation between the computationally estimated electric field and the observed responses in real experiments. Conclusions: Computational pipelines and optimization algorithms have reached a degree of maturity that provides a rationale to improve tES experimental design and a posteriori analysis of the responses for supporting clinical studies. [ABSTRACT FROM AUTHOR]

Comet Interceptor (Comet-I) was selected in June 2019 as the first ESA F-Class mission and aims to explore a pristine comet, which will visit the inner Solar System for the first time. Comet-I will hitch a ride to a Sun-Earth L2 quasi-halo orbit, as a co-passenger in ESA's M4 ARIEL's launch. At the time of writing, this is scheduled for 2029. It will then remain there awaiting the right departure conditions to leave definitively and intercept a newly discovered comet. Comet-I will be the first mission to be designed and launched without an already identified target. Given the latter, the Comet-I Target Identification Working Group is tasked with the continuous follow-up of newly discovered comets, to analyse their characteristics as virtual targets for Comet-I. The goal of the paper is to inform the decision-making process that will eventually be necessary to commit Comet-I spacecraft to its final target. Within this context, this paper presents a trajectory optimization process based on Primer Vector theory that considers intercept trajectories with multiple revolutions and multiple deep space manoeuvres (DSM). The process is applied to 21 long period comets, which were discovered since July 1, 2020 and whose characteristics are of interest for the Comet-I mission. In fact, one of the latest of these recently discovered comets, C/2023H2, is the most accessible virtual target discovered at the time of writing, with delta-V intercept costs well within Comet-I capabilities. A complete launch window analysis is thus presented for C/2023H2, which accounts not only for the most optimal delta-V intercept transfer, but also considers all launch options that satisfy all Comet-I's current boundary conditions and flyby constraints. A systematic launch window analysis is made possible by a grid search approach on which departure and fly-by dates are fixed and the remaining design variables, including the times for manoeuvres and their Δ v impulse vector, are optimized via Primer Vector theory. This allows to understand the latest possible departure date, but also the sensitivity to departure and arrival dates of key encounter characteristics such as relative fly-by speed, the solar aspect angle and the Earth distance. • A Primer Vector Theory-based trajectory optimization tool is shown, tailored for ESA's Comet Interceptor (Comet-I). • The method supports trajectories with multiple revolutions around the Sun and multiple impulses. • The method allows rapid grid search over the search space, finding the lowest cost trajectory and other notable solutions. • A novel analysis of the launch window is presented, contributing to the decision-making process of Comet-I. • A multi-manoeuvre strategy benefits Comet-I's trajectory design, lowering the delta-V cost and improving mission flexibility. [ABSTRACT FROM AUTHOR]

Growing concerns about seismic events enforced structural engineers and architects to embrace the hazardous effect of ground motion in design. To address this, researchers have developed various base isolation (BI) techniques. This study comprehensively reviews BI system types, techniques, and implementation. Exploring the dynamic response of three-dimensional BI devices and the mutual effects of isolation devices and soil-structure interaction during strong ground motion, the paper covers topics such as seismic isolation of nuclear power plants, cost analysis, and various optimization techniques. Furthermore, the paper investigates the behavior of isolation devices in beyond-design events, including blast and aircraft impact loading. In general, the seismic isolation and control device response is demonstrated through shaking table tests and computational analysis. The study sheds light on the functions of seismic isolation system by comparing them with fixed base structures. Additionally, the paper presents codal recommendations, recent advancements, and current practices, aligning them with historical developments and past reviews of different BI techniques, along with their advantages and disadvantages. In conclusion, the closing remarks emphasize the future research prospects in this field. [ABSTRACT FROM AUTHOR]

This paper combines artificial intelligence deep learning technology to optimize the wine making process of mango wine. Moreover, in view of the shortcomings of traditional electronic nose data processing methods, a deep learning method based on SSAE‐BPNN is proposed for electronic nose data processing. In addition, according to the characteristics of automatic learning features, this paper uses a deep learning method based on SSAE‐BPNN to simplify the process of traditional data processing methods. Finally, this paper constructs an electronic nose system that can be used to identify mango wine making characteristics, and enhances the effect of electronic nose recognition through deep learning. Through the analysis, it can be seen that the mango wine making process optimization method based on artificial intelligence deep learning technology proposed in this paper has a certain effect, and it has optimized the traditional mango wine making process. [ABSTRACT FROM AUTHOR]

Despite their widespread applications in sectors such as pulp and paper, textile, food and beverage, pharmaceuticals, and biofuel production, laccases encounter challenges related to their activity and stability under varying reaction conditions. This review accumulates data on the complex interplay between laccase characteristics and reaction conditions for maximizing their efficacy in diverse biotechnological processes. Benefits of organic media such as improved substrate selectivity and reaction control, and their risks such as enzyme denaturation and reduced activity are reported. Additionally, the effect of reaction conditions such as pH and temperature on laccase activity and stability are gathered and reported. Sources like Bacillus pumilus, Alcaligenes faecalis, Bacillus clausii, and Bacillus tequilensis SN4 are producing laccases that are both thermo-active and alkali-active. Additionally, changes induced by the presence of various substances within reaction media such as metals, inhibitors, and organic solvents are also reported. Bacillus pumilus and Bacillus licheniformis LS04 produce the most resistant laccases in this case. Finally, the remarkable laccases have been highlighted and the proper laccase source for each industrial application is suggested. [ABSTRACT FROM AUTHOR]

AbstractThis paper investigates a scheduling imperfect maintenance policy for an operating system that works at random times for multiple jobs (n tandem jobs or n parallel jobs). We consider the system suffers from type-I failure which is corrected by a minimal repair, or type-II failure, which is disaster and is eliminated by a corrective maintenance. To control the deterioration process, preventive maintenance is design to go through at a scheduling time T or the completion of multiple jobs, whichever occurs last. Each maintenance is performed imperfectly, the system improves yet its failure characteristic is also changed after maintenance. Lastly, the system is displaced at the N-th maintenance. On the basis minimizes the mean cost rate, this paper derived the optimal scheduling parameters (T*, n*, N*) analytically and numerically, according to its existence and uniqueness. The models we proposed will provide a general structure for maintenance theory of reliability. [ABSTRACT FROM AUTHOR]

This paper studies the design of foundations built on thick compressible soft soil layers that are reinforced by floating columns. Based on a recent methodology, the suggested design combines the bearing capacity and settlement verifications to provide an optimized improvement area ratio (IAR). Then, an optimized length for the floating columns is obtained by introducing the admissible long-term settlement of the unreinforced compressible sublayers and assuming that the total short-term settlement vanishes at the end of project construction. This paper focuses on the variation in the consolidation settlement of the unreinforced compressible sublayer versus the length of the floating columns. The discussion of this design methodology highlights the feasibility of a potential reinforcement solution when producing a cost-effective design, which assures an optimized IAR within the reinforced upper layer and an optimized length for the floating columns. Using typical case history data, a parametric study showed that reinforcement with end-bearing columns is not required to control the admissible long-term settlement. Instead, the suggested design method enables the determination of the optimized length of the floating columns, which satisfies the admissible residual settlement and consolidation time. The comparison between the proposed results and numerical predictions by Plaxis 2D shows good agreement, which confirms the feasibility of an optimized length for floating columns and avoids the systematic adoption of end-bearing reinforcement in columns. [ABSTRACT FROM AUTHOR]

The adjoint method provides an efficient way to compute sensitivities for system models with a large number of inputs. However, implementing the adjoint method requires significant effort that limits its use. The effort is exacerbated in large-scale multidisciplinary design optimization. We propose the adoption of a three-stage compiler as the method for constructing computational models for large-scale multidisciplinary design optimization to enable accurate and efficient adjoint sensitivity analysis. We develop a new modeling language called the Computational System Design Language that provides an appropriate input to the compiler front end that works well with multidisciplinary models. This paper describes the three-stage compiler methodology and the Computational System Design Language. The proposed solution uses a graph representation of the numerical model to automatically generate a computational model that computes adjoint-based sensitivities for use within an optimization framework. For two engineering models, this approach reduces the amount of user code by a factor of approximately two compared to their original implementations, without a measurable increase in computation time. This paper also includes a best-case complexity analysis that is built into the compiler implementation to allow users to estimate the memory required to evaluate a computational model and its derivatives, which is independent of the compiler back end that ultimately generates the computational model. Future compiler implementations are expected to approach the theoretical best-case memory cost and improve run time performance for both model evaluation and derivative computation. [ABSTRACT FROM AUTHOR]

Non-linear programming optimization method was used to optimize total steam and air consumption in the dryer section of multi-cylinder fluting paper machine. Equality constraints of the optimization model were obtained from specified process blocks considering mass and energy balance relationships in drying and heat recovery sections. Inequality constraints correspond to process parameters such as production capacity, operating conditions, and other limitations. Using the simulation, the process parameters can be optimized to improve the energy efficiency and heat recovery performance. For a corrugating machine, optimized parameters show the total steam use can be reduced by about 11% due to improvement of the heat recovery performance and optimization of the operating conditions such as inlet web dryness, evaporation rate, and exhaust air humidity, accordingly total steam consumption can be decreased from about 1.71 to 1.53 tonnes steam per tonne paper production. The humidity of the exhaust air should be kept as high as possible to optimize the energy performance and avoid condensation in the pocket dryers and hood exhaust air. So the simulation shows the supply air should be increased by about 10% to achieve optimal humidity level which was determined about 0.152 kgH2O/(kg dry air). [ABSTRACT FROM AUTHOR]

Due to the huge amounts of wastewater discharge and the high pollution loads in papermaking industry, many greenhouse gases (GHG) are emitted in the papermaking wastewater treatment process. The wastewater dissolved oxygen (DO) control has been considered as the most cost-effective control solution for GHG reduction in wastewater treatment plants (WWTP). However, the competition between contaminant removal efficiency and GHG reduction hinders the drastic reduction of GHG emissions from WWTP. In this study, based on the established integrated GHG emission model, explicitly considering the total GHG reduction targets on the premise of effluent compliance, an intelligent control scheme has been developed for an activated sludge process in a paper mill. Regarding DO as the controlled variable, the proposed hierarchical optimal proportional-integral (HOPI) control scheme was established consisting of three layers: 1) Layer 1 for the effluent quality estimation, 2) Layer 2 for the optimal DO set point determined by genetic algorithm with the influent variations to obey discharging norms and reduce GHG emissions, 3) Layer 3 for the DO tracking proportional integral (PI) control with the controller parameters adjusted by the back propagation neural network to track the dynamically optimized DO set points. The simulation results showed that, compared with the open-loop (OL) operation (averaged aeration, 10/h), the proposed HOPI control (averaged aeration, 7.9/h) reduced the GHG emissions by 12.54% under the premise of discharging norms, which was mainly attributed to the reduction of the aeration electricity consumption. In contrast, the PI control (averaged aeration, 12.9/h) increased the GHG emissions by 7.48% compared with the OL operation. Thus, the proposed HOPI control strategy has demonstrated potential for the application of GHG reduction in industrial WWTPs. [ABSTRACT FROM AUTHOR]

Due to the huge amounts of wastewater discharge and the high pollution loads in papermaking industry, many greenhouse gases (GHG) are emitted in the papermaking wastewater treatment process. The wastewater dissolved oxygen (DO) control has been considered as the most cost-effective control solution for GHG reduction in wastewater treatment plants (WWTP). However, the competition between contaminant removal efficiency and GHG reduction hinders the drastic reduction of GHG emissions from WWTP. In this study, based on the established integrated GHG emission model, explicitly considering the total GHG reduction targets on the premise of effluent compliance, an intelligent control scheme has been developed for an activated sludge process in a paper mill. Regarding DO as the controlled variable, the proposed hierarchical optimal proportional-integral (HOPI) control scheme was established consisting of three layers: 1) Layer 1 for the effluent quality estimation, 2) Layer 2 for the optimal DO set point determined by genetic algorithm with the influent variations to obey discharging norms and reduce GHG emissions, 3) Layer 3 for the DO tracking proportional integral (PI) control with the controller parameters adjusted by the back propagation neural network to track the dynamically optimized DO set points. The simulation results showed that, compared with the open-loop (OL) operation (averaged aeration, 10/h), the proposed HOPI control (averaged aeration, 7.9/h) reduced the GHG emissions by 12.54% under the premise of discharging norms, which was mainly attributed to the reduction of the aeration electricity consumption. In contrast, the PI control (averaged aeration, 12.9/h) increased the GHG emissions by 7.48% compared with the OL operation. Thus, the proposed HOPI control strategy has demonstrated potential for the application of GHG reduction in industrial WWTPs. [ABSTRACT FROM AUTHOR]

With the rapid development of information technology, scientific social networks (SSNs) have become the fastest and most convenient way for researchers to communicate with each other. Many published papers are shared via SSNs every day, resulting in the problem of information overload. How to appropriately recommend personalized and highly valuable papers for researchers is becoming more urgent. However, when recommending papers in SSNs, only a small amount of positive instances are available, leaving a vast amount of unlabelled data, in which negative instances and potential unseen positive instances are mixed together, which naturally belongs to One-Class Collaborative Filtering (OCCF) problem. Therefore, considering the extreme data imbalance and data sparsity of this OCCF problem, a hybrid approach of Social and Content aware One-class Recommendation of Papers in SSNs, termed SCORP, is proposed in this study. Unlike previous approaches recommended to address the OCCF problem, social information, which has been proved playing a significant role in performing recommendations in many domains, is applied in both the profiling of content-based filtering and the collaborative filtering to achieve superior recommendations. To verify the effectiveness of the proposed SCORP approach, a real-life dataset from CiteULike was employed. The experimental results demonstrate that the proposed approach is superior to all of the compared approaches, thus providing a more effective method for recommending papers in SSNs. [ABSTRACT FROM AUTHOR]

Damage detection and localization in civil engineering constructions using dynamic analysis has become an important topic in recent years. This paper presents a methodology based on non-destructive detection, localization and quantification of multiple damages in simple and continuous beams, and a more complex structure, namely two-dimensional frame structure. The proposed methodology makes used of Firefly Algorithm and Genetic Algorithm as optimization tools and the Coordinate Modal Assurance Criterion as an objective function. The results show that the proposed combination of Coordinate Modal Assurance Criterion and Firefly Algorithm or Genetic Algorithm can be easily used to identify multiple local structural damages in complex structures. However, the convergence rate becomes slower for the case of multiple damages compared to the case of single damage. The effect of noise on the algorithm is further investigated. It is found that the proposed technique is able to detect the damage location and its severity with high accuracy in the presence of noise, although the convergence rate became slower than in the case when no noise is present. It is also found that the convergence rate of Firefly Algorithm is much faster than that of Genetic Algorithm. [ABSTRACT FROM AUTHOR]

The design of a high-volume printer for professional use is rather complex. The design of the paper path and the timing of sheets is frequently reengineered as the design of the printer components progresses. This paper presents an optimization model for the combined paper path and timing design problem. The paper path is an optimal physical dimensioning problem, while the timing is an open-loop optimal control problem. The coupled optimization problem is formulated as a simultaneous analysis and design (SAND) problem using a direct transcription of the optimal control problem. Benefits of the chosen formulation for industrial application are the ease of setting up the optimization model for arbitrary printer configurations, and the short computation times. Results of an industrial case are presented. [ABSTRACT FROM AUTHOR]

This paper studies the secure transmission in a multiuser broadcast channel where only the statistical channel state information of the eavesdropper is available. We propose to apply secret dirty-paper coding (S-DPC) in this scenario to support the secure transmission of one user and the normal (unclassified) transmission of the other users. By adopting the S-DPC and encoding the secret message in the first place, all the information-bearing signals of the normal transmission are treated as noise by potential eavesdroppers and thus provide secrecy for the secure transmission. In this way, the proposed approach exploits the intrinsic secrecy of multiuser broadcasting and can serve as an energy-efficient alternative to the traditional artificial noise (AN) scheme. To evaluate the secrecy performance of this approach and compare it with the AN scheme, we propose two S-DPC-based secure transmission schemes for maximizing the secrecy rate under constraints on the secrecy outage probability (SOP) and the normal transmission rates. The first scheme directly optimizes the covariance matrices of the transmit signals, and a novel approximation of the intractable SOP constraint is derived to facilitate the optimization. The second scheme combines zero-forcing dirty-paper coding and AN, and the optimization involves only power allocation. We establish efficient numerical algorithms to solve the optimization problems for both schemes. Theoretical and simulation results confirm that, in addition to supporting the normal transmission, the achievable secrecy rates of the proposed schemes can be close to that of the traditional AN scheme, which supports only the secure transmission of one user. [ABSTRACT FROM PUBLISHER]

• Steam followed by alkaline treatment enhanced enzyme accessibility of PPS cellulose. • Reducing sugar yield was maximized during batch hydrolysis by optimized conditions. • High solid fed-batch hydrolysis was developed by ideal enzyme addition strategies. • Fed-batch S-SSCF with specified pre-hydrolysis was developed for high ethanol titer. The current study investigated an integrated fermentation process for efficient conversion of pulp and paper sludge (PPS) material to bio-ethanol. To achieve maximum yield of reducing sugar from the pretreated PPS, most influencing parameters such as enzyme dosages, surfactant dosages on PPS loadings were optimized in a batch saccharification process using response surface methodology. The experimental validation studies under optimal conditions of 6% (w/w) solid loading condition, 0.16% (w/w) of surfactant concentration and 158 FPU/gm of enzyme loading resulted in a maximum reducing sugar yield of 45 ± 3.75% (w/w). Separate batch saccharification studies were conducted with 4% to 7% (w/w) solid loading to formulate best operating conditions for fed-batch saccharification with 13% (w/w), 18% (w/w) and 22% (w/w) high solid loading. The PPS solid loading of 18% (w/w) in fed-batch saccharification resulted in maximal release of glucose and xylose of 79.56 g/L and 8.65 g/L respectively at 60 h. Further improvement in the conversion of PPS to bioethanol was established through adoption of fed-batch semi-simultaneous saccharification and co-fermentation (S-SSCF) process. Fermentation using co-cultivation of two yeast species namely P. stipitis NCIM 3499 and Baker's yeast resulted in maximum ethanol concentration of 42.34 g/L with 0.53 g/g yield from 18% (w/w) solid loading. Thus, the current study demonstrated the potential application of PPS waste as a feedstock material for ethanol production, which can be adopted by industries as an environment-friendly alternative to common solid waste management. [ABSTRACT FROM AUTHOR]

This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area. [ABSTRACT FROM AUTHOR]

Improving the control performance of dynamic vibration absorbers has recently been effective by introducing a grounded negative stiffness device. However, the negative stiffness structure is unstable and difficult to achieve in engineering practice , and its major drawback is that it amplif ies the vibration response of the primary system at low frequency region. Meanwhile, some mechanical devices can be combined to make the DVA work even better with a grounded positive stiffness. For this purpose, this paper combines for the first time the control effect of the inerter device and grounded positive stiffness into a three-element DVA model in order to better improve vibration reduction of an undamped primary system under excitation. First, the dynamic equation of motion of the system is written according to Newton 's second law. Then, the steady-state displacement response of the primary system under harmonic excitation is calculated. In order to minimize the resonant response of the primary system around its natural frequency, the extended fixed point theory is applied. Thus, the optimized parameters such as the tuning frequency ratio, the stiffness ratio , and the approximate damping ratio are determined as a function of mass ratio and inerter – mass ratio. From the results analysis, it was found that the inerter – mass ratio has a better working range to guarantee the stability of the coupled system. Then , study on the effect of inerter – mass ratio on the primary system response is carried out. It can be seen that increasing the inerter – mass ratio in the optimal working range can reduce the response of the primary system beyond its uncontrolled static response. However, it is necessary to avoid the situation where the inerter – mass ratio is very large because it can lead to unrealistic optimal parameters. Finally, comparison with other DVA models is show n under harmonic and random excitation of the primary system. It is found that the proposed DVA model in this paper has high control performance and can be used in many engineering practice s. [ABSTRACT FROM AUTHOR]

This paper addresses optimization problems subject to an objective function's evaluation‐number constraint arbitrarily given in advance. These problems are practical for black‐box objective functions which cost much money and/or time per evaluation because money and/or time constraints are typically applicable in real‐world projects. This paper suggests a new solution strategy that can be used to adapt metaheuristics to evaluation‐number constraints arbitrarily given in advance. The proposed strategy updates the setting parameters of a target metaheuristic algorithm for every generation by solving a fixed small‐size optimization problem related to the evaluation‐number constraint. It has the advantage of being insensitive to the effects of dimensionality increases in principle. The effectiveness of the proposed strategy is confirmed by numerical experiments with two well‐known metaheuristics, i.e., the particle swarm optimization algorithm and the differential evolution algorithm. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

This paper presents classification and analysis of the mathematical models of the spread of COVID-19 in different groups of population such as family, school, office (3–100 people), town (100–5000 people), city, region (0.5–15 million people), country, continent, and the world. The classification covers major types of models (time-series, differential, imitation ones, neural networks models and their combinations). The time-series models are based on analysis of time series using filtration, regression and network methods. The differential models are those derived from systems of ordinary and stochastic differential equations as well as partial differential equations. The imitation models include cellular automata and agent-based models. The fourth group in the classification consists of combinations of nonlinear Markov chains and optimal control theory, derived by methods of the mean-field game theory. COVID-19 is a novel and complicated disease, and the parameters of most models are, as a rule, unknown and estimated by solving inverse problems. The paper contains an analysis of major algorithms of solving inverse problems: stochastic optimization, nature-inspired algorithms (genetic, differential evolution, particle swarm, etc.), assimilation methods, big-data analysis, and machine learning. [ABSTRACT FROM AUTHOR]

Locomotive engines serve as the powering heart of rail transportation which is widely used for massive cargo over long distances. The traditional locomotive engines have depended on the internal combustion engine for decades, operated using fossil fuels that negatively impact the environment. Since more and more commodities are transported by trains, the prime engines need to be improved where the fuels are expected to enhance the engine's performance and alleviate the environmental impact. This paper presents a comparison of three developed locomotive engines with different configurations, such as an internal combustion engine combined with a gas turbine and molten carbonate fuel cell (ICE-GT-MCFC), a gas turbine combined with a solid oxide fuel cell and onboard hydrogen production using aluminum electrolysis cell (GT-SOFC), and a GT combined with sold oxide and proton exchange membrane fuel cells (GT–SOFC–PEMFC). The paper then identifies one hybridized engine, which is GT–SOFC–PEMFC, according to its low weight and better performance to conduct an optimization study to enhance its efficiency, reduce the cost, and lower the environmental impact. The present optimization study is undertaken through a multi-objective particle swarm optimization, involving thermodynamic, exergoeconomic, and exergoenvironmental analyses. The optimized GT–SOFC–PEMFC locomotive engine is improved in power from 5.9 MW to 7.3 MW and improved in performance to 49.1% energetic efficiency and 47.0% exergetic efficiency. Additionally, the optimized GT–SOFC–PEMFC engine has less specific fuel and product cost of 24.9 $/GJ and 34.7 $/GJ, respectively, and less specific exergetic environmental impact of fuel and product of 13.2 Pt/GJ and 16.2 Pt/GJ, respectively. Also, the relative cost difference and relative environmental impact difference are significantly reduced by more than 20% to 39.4% and 22.8%, respectively. The operational conditions of the optimized GT–SOFC–PEMFC engine are improved to achieve better efficiency and better cost effectiveness in a more environmentally-benign manner. • Comparison between three hybridized combined locomotive engines is established. • Optimized GT–SOFC–PEMFC engine has 7.3 MW power and 49% energetic efficiency. • Specific exergetic product cost declined to 51 $/GJ for fuel and 35 $/GJ. • Specific exergetic environmental impact for fuel and product becomes 13 and 16 Pt/GJ. • The relative environmental impact difference becomes 22.8% instead of 44.3%. [ABSTRACT FROM AUTHOR]

Metal additive manufacturing is an active field of innovation. However, for laser power bed fusion (LPBF), supports removal is a major constraint. In this technology, supports are strongly welded to the part to tightly maintain it, avoid distortion, and evacuate thermal load. Although supports are usually optimized for manual removal, machining is often necessary, which can affect post-processing productivity. This paper proposes a comprehensive methodological approach to optimize the selection of cutting parameters, cutting tools, and support structure for LPBF. The aim is to help additive manufacturers find supports that reduce machining costs in terms of time and cutting tool degradation, from among the numerous support designs available. This approach can also optimize the design of lattice structures used inside parts. Our results show that among the 11 designs tested, honeycomb and squared pattern grid supports are the most efficiently machined using the 8-teeth tangential milling of the 3 tools tested, with a good post-machined surface roughness and tools' health. The method considers low magnification optical analysis and an accelerometer sensor, which is easy to use even for small- and medium-sized enterprises. This paper also proposes and analyzes a new kind of porous support using this method. [ABSTRACT FROM AUTHOR]

Ever-increasing penetration of distributed energy resources (DERs) in the power grids, alongside their numerous benefits, brings new challenges that call for enhanced solutions in the field of control and management of power grids. The majority of the available research have considered either distribution or transmission grids in their studies. In this paper, a comprehensive review of the effects of DERs on the distribution and transmission grids is performed. The focus of this paper is on hierarchical management methods in order to categorize different approaches and highlight the gaps. Moreover, a review is conducted in the field of the newly introduced distributed energy resources management system (DERMS) concept. A DERMS can facilitate the hierarchical energy management procedure due to its functionalities and broad capabilities. Hence, its implementation in energy management and its impact on the power grid will be assessed with the aid of a co-simulation platform that considers both transmission and distribution grids. [ABSTRACT FROM AUTHOR]