Statistical Optimization Method Research Articles

Grid Integration Strategies for Optimizing Renewable Energy Deployment and Grid Resilience

This study explores the integration of renewable energy sources, namely, solar and wind, focusing on strategies to optimize their deployment into the electrical grid, and increasing the resiliency of the grid. Using four-year comprehensive data from Spain, including energy consumption, generation, pricing, and the condition of the weather, advanced statistical analysis, regression models, and optimization methods have been employed. Based on the results, it is clear that solar energy is seasonal, and wind energy is variable, with the weather playing a considerable role in the energy output. The optimization analysis showed that when the renewable capacity was increased to include 30 MW of solar and 120 MW of wind, the energy demand would be met at a significantly lower total system cost of $12.60 per unit. The costs related to operation and emissions would also decrease notably. However, with the regression models giving modest values of R² equal to 0.19 for solar and R² equal to 0.21 for wind, the extent of these developments and prediction can be fairly modest. Still, these results provide a strong backbone for the prediction of energy generation and show that modernization of the grid and adaptive management are of crucial importance. The results of the study could provide a guideline for policymakers and energy managers on how these goals can be achieved.

A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies

BackgroundIn microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.ResultsOur simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.ConclusionsTight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

Fracture risk assessment in the presence of competing risk of death.

To identify the optimal statistical approach for predicting the risk of fragility fractures in the presence of competing event of death. We used real-world data from the Dubbo Osteoporosis Epidemiology Study that has monitored 3035 elderly participants for bone health and mortality. Fragility fractures were ascertained radiologically. Mortality was confirmed by the State Registry. We considered four statistical models for predicting fracture risk: (i) conventional Cox's proportional hazard model, (ii) cause-specific model, (iii) Fine-Gray sub-distribution model, and (iv) multistate model. These models were fitted and validated in the development (60% of the original sample) and validation (40%) subsets, respectively. The model performance was assessed by discrimination and calibration analyses. During a median follow-up of 11.3 years (IQR: 7.2, 16.2), 628 individuals (34.5%) in the development cohort fractured, and 630 (34.6%) died without a fracture. Neither the discrimination nor the 5-year prediction performance was significantly different among the models, though the conventional model tended to overestimate fracture risk (calibration-in-the-large index = - 0.24; 95% CI: - 0.43, - 0.06). For 10-year risk prediction, the multistate model (calibration-in-the-large index = - 0.05; 95% CI: - 0.20, 0.10) outperformed the cause-specific (- 0.23; - 0.30, - 0.08), Fine-Gray (- 0.31; - 0.46, - 0.16), and conventional model (- 0.54; - 0.70, - 0.39) which significantly overestimated fracture risk. Adjustment for competing risk of death has minimum impact on the short-term prediction of fracture. However, the multistate model yields the most accurate prediction of long-term fracture risk and should be considered for predictive research in the elderly, who are also at high mortality risk. Fracture risk assessment might be compromised by the competing event of death. This study, using real-world data found a multistate model was superior to the current competing risk methods in fracture risk assessment. A multistate model is considered an optimal statistical method for predictive research in the elderly.

Branch spiral beam harvester for uni-directional ultra-low frequency excitations

In recent years, there has been a growing interest in piezoelectric energy harvesting systems, particularly for their potential to recharge or replace batteries in energy-efficient electronic devices and wireless sensor networks. Nonetheless, the conventional linear piezoelectric energy harvesters (PEH) face limitations in ultra-low frequency vibrations (1–10 Hz) due to their narrow operating bandwidth and higher resonance frequencies. To address this, researchers explored compact shaped geometries, with spiral PEH being one such design to lower resonance frequencies by reducing structural stiffness. While trying to achieve this lower resonance frequency, spiral designs overlooked that they were spreading the stress across the structure. This was a significant drawback because it reduced the structure's ability to stress the piezoelectric transducer. The issue remains unaddressed, limiting the power generation of spiral beam harvesters. Furthermore, spiral structures also fail to broaden the operating bandwidth, posing additional constraints on their effectiveness. This study introduces a novel solution – the “branch spiral beam harvester,” combining the benefits of both spiral and branch beam designs. The integration of the branch beam concept into the spiral structure aimed to broaden the effective frequency range and establish a concentrated stress area for the placement of the piezoelectric transducer. Finite Element Analysis (FEA) was employed to assess operating bandwidth and stress distribution, while experimental studies evaluated voltage and power generation. Once the workability was confirmed, a statistical optimisation method was introduced to tailor the harvester for specific frequencies in the ultra-low frequency range. Results indicated that the branch spiral beam harvester exhibits a wider operating bandwidth with six natural frequencies in the ultra-low frequency range. It harnessed significantly higher output voltages and power compared to conventional linear PEH. This innovation presents a promising advancement in piezoelectric energy harvesting, offering improved performance without the need for proof masses or additional accessories.

Impact of process parameter on the behavior of pyrocarbon deposition in chemical vapour infiltration (CVI) process

Carbon-carbon composite manufactured by deposition of pyrocarbon (PyC) through chemical vapor infiltration (CVI) has the key issue of being process parametric sensitive which necessitates the detailed study of the effect of process parameters on the rate of PyC deposition. Conventional method of studying the parametric effect by changing one variable at a time keeping the other variables constant has a limitation of more number of experiments and missing the interaction effect among the variables. Here, the effect of process parameters including temperature, pressure, methane gas flow rate, and nitrogen gas flow rate on the mass gain and PyC deposition was studied by Taguchi method, a statistical optimization method, which has the advantage of very few experiments performed at specific pairs of process parameters only. The experiments were performed at three levels of the process parameters. Carbon-Carbon composite material is processed through the CVI process where PyC was deposited on porous carbon fiber preforms at various process conditions as per the Taguchi method. The impact of gas residence time, Reynolds number, Prandtl number, and Peclet number were also investigated. It was observed that the CVI process parameters significantly affect the rate of PyC deposition. Optimized CVI process parameters are essential for achieving a high rate of PyC deposition to reduce the processing time. The findings have revealed that a higher PyC deposition rate arises under high temperatures, pressure, methane gas flow rate, and optimal nitrogen gas flow rate. The effect of the critical interaction of the CVI process parameters on the rate of PyC deposition was also obtained. Based on the experimental studies, process guidelines are proposed for the densification of carbon fibers preform to realize C/C composite products.

An innovative electrolytic cation exchange reactor system for cleaner generation of hydrogen gas using ocean water

The study presents a novel integrated three-compartment electrochemical continuous electrodeionization reactor that was constructed in a laboratory environment. In order to extract large amounts of carbon dioxide from naturally occurring oceanwater in the form of bicarbonate and carbonate, it develops a innovative electrolytic cation exchange process. At the same time, it produces hydrogen gas for possible hydrocarbon synthesis. The study employs the Box-Behnken experimental design approach implemented through the Design Expert software to investigate and optimize the performance of electrochemical hydrogen extraction from Ocean water from an integrated reactor. The integrated electrochemical continuous-type electrodeionization reactor comprises two cation-permeable membranes that operate as boundaries between three compartments: a central compartment and electrode compartments that house the cathode and anode, each of which may reverse polarity. These membranes are made of gel polystyrene cross-linked with divinylbenzene, which has acidic properties that allow cation transport while inhibiting anions. The integrated reactor's electrode chambers are enclosed in plexiglass end plates and contain distinct Plexiglass electrode compartments. These compartments have mesh and solid steel anode and cathode electrodes, with an electrode active area of 176.71 cm2. The influence of various factors, such as voltage and electrolyte amount, on the production of hydrogen synthesis is examined. The study results demonstrate a maximum hydrogen production rate of 2.2 mg/min and a hydrogen production efficiency of 7.82%. The lab testing was carried out using experimental equipment to determine viability. The tests included 35-min runs that measured hydrogen generation under various parameters, such as applied voltage, electrolyte concentration, and pH levels. The ocean salt solutions of 0.5M, 1M, and 2M were initially prepared and evaluated for hydrogen generation. Each concentration is assessed at a voltage range of 4–15V, with matching solution pH values ranging from 2 to 7. Optimization studies were conducted to enhance the hydrogen production rate. The results indicate that hydrogen production rises proportionally with applied voltage and electrolyte concentration but falls with pH growth. Within the Design Expert software, the statistical methods for response surface analysis and optimization are utilized accordingly.

BosonSampling.jl: A Julia package for quantum multi-photon interferometry

We present a free open source package for high performance simulation and numerical investigation of boson samplers and, more generally, multi-photon interferometry. Our package is written in Julia, allowing C-like performance with easy notations and fast, high-level coding. Underlying building blocks can easily be modified without complicated low-level language modifications. We present a great variety of routines for tasks related to boson sampling, such as statistical tools, optimization methods and classical samplers. Special emphasis is put on validation of experiments, where we present novel algorithms. This package goes beyond the boson sampling paradigm, allowing for the investigation of new interferometric behaviours such as bosonic bunching.

Ultrasonic welding of Cork Wood/PLA composites: effect of welding factors on lap shear strength performance

Ultrasonic welding of Cork Wood/PLA composites: effect of welding factors on lap shear strength performance

Polyhydroxybutyrate (PHB) bioplastic characterization from the isolate Pseudomonas stutzeri PSB1 synthesized using potato peel feedstock to combat solid waste management

The global fossil-based plastic industry is at a crucial point, requiring bio-based polymers. Polyhydroxybutyrate (PHB) is formed under nutritional stress conditions; however, efficient bacteria and cheap substrates are needed. This study found that potato peel, a cheap and abundant carbon source, can improve PHB production. PHB crotonic assay screened 11 Sudan Black B and Nile red positive isolates for PHB production. PSB1, a Pseudomonas stutzeri PSB1 (MN539619) yields 2.39 g/L maximal PHB. PHB generation from high-carbon agro-wastes using Pseudomonas stutzeri PSB1was 3.09 g/L for potato peel. Plackett-Burman and CCD, a statistical optimization method, increased PHB production. After optimizing all CCD variables, potato peel, Urea, and RPM were found to increase PHB production (6.1 g/L). The optimized parameters extracted 3.98 g/L of PHB from 6.36 g/L of dry biomass of Pseudomonas stutzeri PSB1. FT-IR, NMR, TGA, and DSC were used to characterize and compare the extracted PHB biopolymer to Sigma Aldrich PHB powder. Extracted PHB-bioplastic was tested for tensile strength and Elongation at break. Based on the above criteria, the recovered PHB biopolymer from Pseudomonas stutzeri PSB1 is a good alternative to agricultural mulch films and non-biodegradable plastics.

Adopting Optimal Statistical Practices: Graduate Programs.

In 2024, the Methodology Corner will briefly look at how we might foster enduring commitments from nurse education researchers to remain up-to-date on their statistical expertise and to use optimal statistical methods. The first column of the year specifically looks at how graduate programs might enrich their statistics curriculum in a manner that could foster an increasingly valid science of nursing education. [J Nurs Educ. 2024;63(3):197-198.].

Developing a model for waste plastic biofuels in CRDi diesel engines using FTIR, GCMS, and WASPAS synchronisations for engine analysis

The excessive use of single-use plastic products in modern life has caused severe environmental, social, economic, and health consequences globally. Mostly all plastics manufactured are one-time-use materials that end up in landfills or as unmanageable garbage. This situation has led to the production of around 400 million tonnes of plastic waste per year, and if this trend continues, global production will reach up to 1100 million tonnes by 2050. India alone produced over 34.7 lakh tonnes per annum (TPA) of plastic waste, with only half of it being recycled or co-processed. As such, there is an urgent need to develop ways to reduce plastic waste. One possible solution is the use of waste plastic biofuel in engines, which has been shown to have promising results. The study aimed to analyse waste plastic oil using (Gas Chromatography Mass Spectrometry) GC-MS and (Fourier Transform Infrared Spectroscopy) FTIR analysis to identify its chemical composition. The findings of the study revealed the presence of various chemical compounds, such as alcohol, hydroperoxide, carbonyl acid groups, ester, carboxylic acid, ketones, aldehyde groups, and others. FTIR analysis confirmed the presence of alcohol, hydroperoxide, carbonyl acid groups, methyl and methylene groups, ester, carboxylic acid, ketones, aldehyde group, symmetric and asymmetric C-H bending, C-O stretch for ethers, carboxylic acids, and esters, and=C-H bending out for alkenes. The study further explains that primary plastic consumption and packaging lifetime have a significant impact on plastic waste generation. The research indicates the need to explore alternative ways to recycle and dispose of single-use plastics to mitigate its negative impact on the environment. Furthermore, this study analyses the statistical optimisation method to develop a model fit for engine behaviour using waste plastic biofuel on a single-cylinder (common rail direct injection engine) CRDi diesel engine using the (weighted aggregated sum product assessment) WASPAS approach. Additionally, the objective is to develop a model that can optimise the engine's performance while using waste plastic biofuel. The uncertainty analysis demonstrated that the experiment was carried out with a high degree of accuracy and the results were reliable. The study employed the WASPAS methodology to evaluate the performance of different (waste plastic oil) WPO samples, and the results showed that the optimal parametric setting to obtain the desired responses can be achieved with a fuel blend of 5%, load of 21 bar, and speed of 2000 RPM. However, the results demonstrate that the use of waste plastic biofuel can significantly improve engine performance, and the proposed optimisation model can accurately predict the engine's behaviour. The regression equation that was formulated showed a reasonable degree of agreement between the actual experimental results and the predicted values, thereby indicating the reliability of the experiment. Significant effects were observed from fuel blend, and speed, whereas load did not make a substantial contribution. The findings regarding the effect of parameters suggest that a reduction in fuel blend, and engine speed resulted in a decline in the performance index, while variations in load had little impact. The relationship between load and speed demonstrates that a rise in load and a reduction in speed contributed to enhanced combustion and a higher performance index. The interaction among fuel blend and speed, with a particular emphasis on the significance of reduced fuel blend and speed values in order to optimise the performance index. The findings of the analysis underlined the vitality of process parameters, specifically fuel blend and speed, wherein speed exhibited a significant impact on the outcomes. The study concludes that the use of waste plastic biofuel in engines can be an effective way to reduce plastic waste while improving engine performance. This study's findings can be applied to various engines to improve their performance while reducing plastic waste. All in all, the outcomes of the study make a substantial contribution to the advancement of scientific information regarding the properties of waste plastic oil as well as its combustion characteristics. This expands the potential for advanced breakthrough innovations in sustainable energy solutions and the conservation of the environment.

Integrated multi-objective optimization of sodium bicarbonate pretreatment for the outer anatomical portion of corncob using central composite design, artificial neural networks, and metaheuristic algorithms

Effective pretreatment of raw biomass is crucial in establishing a successful biorefinery process, and response surface methodology (RSM), a statistical optimization method, is routinely employed for optimizing pretreatment methodologies. However, relying solely on statistical methods fails to capture the nonlinear relationships between pretreatment methodology and lignocellulose recalcitrance. Machine learning (ML) approaches excel at understanding these relationships. Therefore, combining statistical and ML optimization can establish better pretreatment parameters. This study presents a multi-stage approach to identify and optimize a chemical pretreatment for the outer anatomical portion of corncobs (CO). Various chemicals were screened using fixed factor screening and regular two-level factorial design (2FI), and among them NaHCO3 was chosen for further optimization, using central composite design (CCD). Moderate delignification (33.96%), minimum sugar loss (5.26 mg/g of CO), and a high enzymatic saccharification yield of up to 82% were achieved with the design. Three distinct metaheuristic algorithms, namely Teaching-Learning-Based Optimization (TLBO), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA) were employed to optimize three specific hyperparameters of the artificial neural network (ANN), to create hybrid metaheuristic-optimized ANN models, which were subsequently used to validate the results obtained from the CCD. Among the different models, The TLBO-optimized ANN model provided the most accurate predictions than the CCD.

Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach

This article presents a comprehensive data-driven approach on enhancing grid-connected microgrid grid resilience through advanced forecasting and optimization techniques in the context of power outages. Power outages pose significant challenges to modern societies, affecting various sectors such as industries, households, and critical infrastructures. The research combines statistical analysis, machine-learning algorithms, and optimization methods to address this issue to develop a holistic approach for predicting and mitigating power outage events. The proposed methodology involves the use of Monte Carlo simulations in MATLAB for future outage prediction, training a Long Short-Term Memory (LSTM) network for forecasting solar irradiance and load profiles with a dataset spanning from 2009 to 2018, and a hybrid LSTM-Particle Swarm Optimization (PSO) model to improve accuracy. Furthermore, the role of battery state of charge (SoC) in enhancing system resilience is explored. The study also assesses the techno-economic advantages of a grid-tied microgrid integrated with solar panels and batteries over conventional grid systems. The proposed methodology and optimization process demonstrate their versatility and applicability to a wide range of microgrid design scenarios comprising solar PV and battery energy storage systems (BESS), making them a valuable resource for enhancing grid resilience and economic efficiency across diverse settings. The results highlight the potential of the proposed approach in strengthening grid resilience by improving autonomy, reducing downtime by 25%, and fostering sustainable energy utilization by 82%.

Beyond Bias Minimization: Improving Intelligence with Optimization and Human Augmentation

For the last half-century, the U.S. and Allied Intelligence Community (IC) has sought to minimize the ostensibly detrimental effects of cognitive biases on intelligence practice. The dominant approach has been to develop structured analytic techniques (SATs), teach them to analysts in brief training sessions, provide the means to use SATs on the job, and hope they work. The SAT approach, however, suffers from severe conceptual problems and a paucity of support from scientific research. For example, a highly promoted SAT—the analysis of competing hypotheses—was shown in several recent studies to either not improve judgment quality or to make it worse. This article recaps the key problems with the SAT approach and sketches some alternative interventions. At the core of these proposals is the idea that intelligence agencies should be focused broadly on improving intelligence and not narrowly on minimizing bias. While the latter contributes to achieving the former, overemphasis on bias minimization could inadvertently bias agencies toward a singular form of intervention, blinding then from potentially more effective interventions. Two lines of alternative intervention are sketched. The first line focuses on postanalytic statistical optimization methods such as recalibration and performance-weighted aggregation of analysts’ judgments. The second line focuses on a broad human augmentation program to optimize human cognition through better sleep, exercise, nutrition (including nootropic compounds), and biometric tracking. Both lines of effort would require substantial scientific investment by the IC to examine risks and efficacy.

Statistical Optimization of Underwater Lower-Frequency Sound Insulation for Locally Resonant Sonic Material Using Genetic Algorithm

The locally resonant sonic material (LRSM) is an artificial metamaterial that can block underwater sound. The low-frequency insulation performance of LRSM can be enhanced by coupling local resonance and Bragg scattering effects. However, such method is hard to be experimentally proven as the best optimizing method. Hence, this paper proposes a statistical optimization method, which first finds a group of optimal solutions of an object function by utilizing genetic algorithm multiple times, and then analyzes the distribution of the fitness and the Euclidean distance of the obtained solutions, in order to verify whether the result is the global optimum. By using this method, we obtain the global optimal solution of the low-frequency insulation of LRSM. By varying parameters of the optimum, it can be found that the optimized insulation performance of the LRSM is contributed by the coupling of local resonance with Bragg scattering effect, as well as a distinct impedance mismatch between the matrix of LRSM and the surrounding water. This indicates coupling different effects with impedance mismatches is the best method to enhance the low-frequency insulation performance of LRSM.

Design of cell expansion processes for adherent-growing cells with mDoE-workflow.

Adherent cells, mammalian or human, are ubiquitous for production of viral vaccines, in gene therapy and in immuno-oncology. The development of a cell-expansion process with adherent cells is challenging as scale-up requires the expansion of the cell culture surface. Microcarrier (MC)-based cultures are still predominate. However, the development of MC processes from scratch possesses particular challenges due to their complexity. A novel approach for the reduction of development times and costs of cell propagation processes is the combination of mathematical process models with statistical optimization methods, called model-assisted Design of Experiments (mDoE). In this study, an mDoE workflow was evaluated successfully for the design of a MC-based expansion process of adherent L929 cells at a very early stage of development with limited prior knowledge. At the start, the analytical methods and the screening of appropriate MCs were evaluated. Then, cause-effect relationships (e.g., cell growth related to medium conditions) were worked out, and a mathematical process model was set-up and adapted to experimental data for modeling purposes. The model was subsequently used in mDoE to identify optimized process conditions, which were proven experimentally. An eight-fold increase in cell yield was achieved basically by reducing the initial MC concentration.

Performance of Frequency-Corrected Precipitation in Ungauged High Mountain Hydrological Simulation

Accurate precipitation data are essential for understanding hydrological processes in high mountainous regions with limited observations and highly variable precipitation events. While frequency-corrected precipitation data are expected to aid in understanding hydrological processes, its performance in ungauged high mountain hydrological simulation remains unclear. To clarify this issue, we conducted a numerical experiment that used reanalysis precipitation, frequency-corrected precipitation, and gridded precipitation to drive a distributed cold region hydrological model. We selected an ungauged basin in high mountain Asia (Manas River Basin in China) as the study area and employed a statistical parameter optimization method to avoid subjectivity in parameter selection. Our findings indicate that the frequency information from the few existing stations can aid in correcting the reanalysis precipitation data. The frequency correction approach can reduce the total volume of errors in the reanalysis precipitation data, especially when severe biases occur. Our findings show that frequency-corrected precipitation performs better in modeling discharge, runoff depth, and evaporation. Furthermore, the improvement in precipitation using frequency correction bears clear altitude differences, which implies that having more stations at different altitudes is necessary to measure precipitation accurately in similar areas. Our study provides a feasible flow for future precipitation preparation for similar ungauged high mountain areas. Frequency correction, instead of direct interpolation, may be a viable option for precipitation preparation. Our work has reference implications for future hydrological simulations in similar ungauged high mountains.

Use of Multivariate Analysis in Screening for Drought Tolerance in Ornamental Asteraceae Species

Asteraceae is one of the families with a large number of ornamental plants. Climate change imposes the need to select species that are more tolerant to changing environmental conditions, especially drought. In this study, we compared the performance under water stress of six species belonging to different tribes of Asteraceae with different geographical origins. Young plants obtained after seed germination were subjected to intermediate water stress (irrigation with half the water amount used in control treatments) and severe water stress (no irrigation at all) for one month. Growth variables and biochemical stress markers were determined to assess the effects on the plants of the stress treatments. Multivariate analysis tools were used to rank species according to their tolerance. Three species were relatively more susceptible to water stress, Callistephus chinensis, Xerochrysum bracteatum, and Calendula officinalis, whereas Leucanthemum vulgare, Glebionis carinata, and Ageratum houstonianum were more tolerant. Our study indicated that the last two species, which are registered as invasive in some geographic areas, possess a larger phenotypic plasticity. Principal component analysis (PCA) combined with canonical variation analysis (CVA) proved optimal statistical methods for analysing species of diverse origins and belonging to different genera of a large family, such as the Asteraceae.

Optimizing a Biowaste Treatment Design for Energy and Environmental Efficiency of an Integrated Farming System in Tien Giang Province

Agricultural wastes can be converted into biofuels. To meet the requirements of biofuels, the conditions of their manufacturing process for these products must be considered. The purpose of this research is to optimize experimental designs for converting biomass into energy products like pyrolysis and pelleting process. The products were optimized in terms of their energy performance while lowering CO2 emissions. In Tien Giang province, we experimented with pyrolysis and pelleting biowastes derived from an integrated farming system in acid sulfate soil. The experimental factors influencing the energy content of products were determined using statistical analysis and optimization methods, and the potential CO2 emissions from the products were calculated. The optimization results revealed that the ideal pyrolysis parameters were 120 minutes of combustion time at 400oC and 30% moisture content of orchard pruning residues. The best pellets were obtained by combining biogas sludge and biochar from mango pruning residues. These products will store approximately 900 kilograms of CO2 emissions a year. The optimized processes would benefit the environment and bring profits to the system.

Pelletization and characterization of agricultural by-products for water uptake

Industrial gas streams contain moisture of different levels and lowering their humidity is essential to ensure compliance with standards. Sorption has been an effective method to selectively remove water vapor from gaseous mixtures. Although various types of adsorbents have been employed in adsorption, cost-effective agriculture by-products, such as oat hull (OH), canola meal (CM) and their composite, are efficient materials for developing biosorbents. For industrial application, it is crucial to pelletize the biosorbents to control their size, shape, water uptake , and related qualities. This work investigated the effects of die temperature, compressive force, and biomass type on biosorbent pellet quality and interactions. In this regard, raw oat hull and canola meal and their 50%− 50% composition were pelletized under three levels of compressive (2000, 3000 and 4000 N) at 75, 85 and 95 °C as the die temperature. Increasing die temperature and compressive force enhanced quality of the produced pellets. Canola meal was superior in water uptake, while oat hull was important in strengthening pellets. Moreover, sodium lignosulfonate (LS Na ) as an additional binder improved pellet quality. By statistical optimization methods, four pelletized biosorbents (18.6% OH-81.4% CM-Without LS Na ; 100% CM-4% LS Na ; 54.5% OH- 45.5% CM-4% LS Na ; 91.9% OH-8.1% CM-4% LS Na ) were developed, which were promising for industrial application. The optimized biosorbents possessed relatively high density and strength, and their water uptake capacity was in the range of 0.32 – 0.38 g/g, which is comparable to or higher than commercial adsorbents. Therefore, the introduced biosorbents could be effective alternatives to the conventional materials for gas dehydration. • Canola meal and oat hull demonstrated relatively high water uptake capacity. • Pelletization enhanced raw biomass quality and produced uniform biosorbents. • Sodium lignosulfonate improved pellet quality and water uptake. • Four optimized biosorbents were developed and characterized for industrial usage.