Performance Of Optimization Process Research Articles

High-volume utilization of circulating fluidized bed fly ash for the production of autoclaved aerated concrete: Performance optimization and hydration characteristics

Circulating fluidized bed fly ash (CFA) is a waste emitted from coal combustion process in circulating fluidized bed boilers. The high SO3 and f-CaO content severely limits its application in building materials. The purpose of this study is to explore the possibility of large-scale application of CFA in autoclaved aerated concrete (AAC). The preparation parameters of AAC were discussed and the performance was optimized. Moreover, the workability and environmental properties of AAC were explored. The results indicated that AAC was well adapted to high CFA content, although a high CFA content caused a higher water demand as well as a reduction in the silicon content. The introduction of quartz sand and blast furnace slag effectively addressed the problems of insufficient active silica and high specific gravity. When the CFA doping was 50 %, H4 sample showed the best mechanical properties (3.82 MPa and 641 kg/m3), which meet the requests of A3.5, B06 in the Chinese national standard GB/T 11968–2020. Moreover, the toxicity leaching results showed that H4 sample was green and harmless. During the performance optimization process, more well-crystallized tobermorite was produced, whose crystal form changed from acicular to slat. Massive C-S-H gels and better crystallinity of tobermorite were observed, where were bonded and intercalated with each other, thus enhancing the properties. The paper may provide guidance for high dosing of CFA in AAC.

Membrane-based carbon capture for waste-to-energy: Process performance, impact, and time-efficient optimization

The energy crisis and rising waste production results in the need for more waste-to-energy solutions. However, capturing fossil-based carbon from waste incineration is crucial. The power consumption and overall impact of the carbon capture process are essential for the identification of the most suitable solution. The aim of this paper is to promote a time-efficient optimization, optimize a membrane-based post-combustion carbon capture process, and quantify its impacts on a waste-to-energy plant for various system configurations with different levels of CO2 recovery and purity. The proposed robust evaluation of the system with non-linearities resulted in the utilization of genetic algorithms with subsequent verification. Quantifying power consumption allows the comparison of different carbon capture technologies. The results confirm the importance of process optimization, show the influence of individual parameters, and quantify the disproportionate drop in power consumption with decreasing target CO2 recovery. The power consumption can be as low as 1.14 GJ/tonne of CO2 for CO2 purity of 95 % and recovery of 50 % and 1.66 GJ/tonne of CO2 for CO2 purity of 95 % and recovery of 90 %. The results also suggest that carbon neutrality can be achieved without compromising the R1 efficiency classification of energy recovery.

Removal of Ammonia and Colour from Landfill Leachate using Integrated Electrocoagulation-Zeolite Adsorption Processes

The amount of waste generated in Malaysia had increased annually due to both economic and population accelerations. This resulted in an increase in leachate production that contains a high amount of ammonia nitrogen (NH3-N) and colour, which is a severe problem for both environments as well as for human health. Therefore, this study aimed to determine the potential of Electrocoagulation (EC) – zeolite adsorption for removing NH3-N and colour from landfill leachate. In the experimental works, two batch studies consist of batch EC using aluminium (Al) cylindrical electrode and batch adsorption study using clinoptilolite zeolites adsorbent were conducted using leachate sample collected from Pulau Burung Sanitary Landfill (PBSL). The batch EC experiment was carried out to determine the optimum removal of NH3-N and colour by the effect of current density (7 - 35 mA/mm2), initial pH (5 - 9), electrolysis time (0 - 90 minutes) and inter-electrode distance (5 - 25 mm). An integrated EC - zeolite adsorption was carried out under the optimum condition of 2.0 g/150 mL adsorbent dosage, contact time of 100 minutes and an initial pH value of 8. Based on the experiment, almost 50% of NH3-N and 95% of colour were removed using a single EC under the optimum current density of 14.0 mA/mm2, pH 5, electrolysis time of 75 minutes and 15 mm of inter-electrode distance. A comparison of colour removal between single EC and EC-Zeolite showed the same degradation performance. The interaction of hydrogen ion (H+) with aluminium hydroxide ion (Al (OH)3) produces during EC stabilises small particles forming larger sludge that reduces colour concentration. The removal of NH3-N increased from 50.0% to 93% when EC was integrated with clinoptilolite zeolite adsorption. In conclusion, the results show that both processes can potentially remove colour while the integrated process effectively removes NH3-N from landfill leachate. The proposed treatment helps optimise process performance while minimising the operational cost of the treatment. Thus, the proposed integrated EC – zeolite adsorption processes can be an alternative to landfill leachate treatment which aligns with the 12thMalaysia Plan to reduce pollution and carbon emissions to become a carbon neutral nation as early as 2050.

Optimization of methane gas-liquid mass transfer during biogas-based ectoine production in bubble column bioreactors

Nowadays, the utilization of biogas for energy generation is hindered by the declining production costs of solar and wind power. A shift towards the valorization of biogas into ectoine, a highly valuable bioproduct priced at 1000 €⸱kg−1, offers a novel approach to fostering a more competitive biogas market while contributing to carbon neutrality. This study evaluated the optimization of CH4 gas-liquid mass transfer in 10 L bubble column bioreactors for CH4 conversion into ectoine and hydroxyectoine using a mixed methanotrophic culture. The influence of the empty bed residence time (EBRTs of 27, 54, and 104 min) at different membrane diffuser pore sizes (0.3 and 0.6 mm) was investigated. Despite achieving CH4 elimination capacities (CH4-ECs) of 10–12 g⸱m−3⸱h−1, an EBRT of 104 min mediated CH4 limitation within the cultivation broth, resulting in a negligible biomass growth. Reducing the EBRT to 54 min entailed CH4-ECs of 21–24 g⸱m−3⸱h−1, concomitant to a significant increase in biomass growth (up to 0.17 g⸱L⸱d−1) and reaching maximum ectoine and hydroxyectoine accumulation of 79 and 13 mg⸱gVSS−1, respectively. Conversely, process operation at an EBRT of 27 min lead to microbial inhibition, resulting in a reduced biomass growth of 0.09 g⸱L⸱d−1 and an ectoine content of 47 mg⸱gVSS−1. While the influence of diffuser pore size was less pronounced compared to EBRT, the optimal process performance was observed with a diffuser pore size of 0.6 mm.

A Comprehensive Review of the Rheological Properties of Powders in Pharmaceuticals

The perpetual significance of the pharmaceutical industry in society necessitates ongoing research efforts to enhance the efficacy of its manufacturing processes. Given that drug product manufacturing typically involves powder processing, a thorough understanding of powder characterization is needed for optimal process performance. Powder rheology is commonly examined in pharmaceutical manufacturing to elucidate the relationship between powder properties and the performance of pharmaceutical processes. This paper provides a brief discussion of recent literature regarding the various powder properties and characterization techniques encompassed in powder rheology. The powder properties are categorized into particle size, particle morphology, friability, electrostatics, permeability, wettability, cohesion, bulk density, and agglomeration sections. A distinct focus is placed on the segment about powder wettability. This review informs readers about the fundamental properties of powders known to influence pharmaceutical processes. It discusses the interrelationships among these properties, powder characterization techniques, and ideal states of powder properties that lead to optimal process performance.

Interactions between bacteria and microalgae in microalgal-bacterial symbiotic wastewater treatment systems: mechanisms and influencing factors

Microalgal-bacterial symbiotic wastewater treatment systems (MBSWTSs) have received widespread attention due to their capacity to achieve high pollutant removal efficiency during wastewater treatment, with low energy consumption requirements, efficient carbon sequestration, and wastewater resource utilization. This paper provides an overview of the treatment performance and current research status of MBSWTSs, including a detailed summary of the mechanisms of nitrogen, phosphorus, and carbon removal by MBSWTSs and the interactions between bacteria and microalgae. In particular, this review focuses on the influence of operational parameters on the regulation of the symbiotic system, such as the microalgal:bacterial ratio, N:P ratio, external carbon source, dissolved oxygen concentration, aeration mode, and light availability. Among these factors, the microalgal:bacterial ratio, carbon source, and light availability have an important influence on microalgal-bacterial competition, as well as the trophic mode of the system, biomass production, and the capacity for the process to be practically applied on a large scale. MBSWTSs still have some challenging aspects that have hindered their development and application, such as the unknown mechanism of microalgal-bacterial co-metabolism, limited previous practical applications, algal contamination, and harvesting difficulties. To overcome these challenges, future research requires a multidisciplinary approach, incorporating life sciences, material science, and other disciplines. Comprehensive research should be conducted on the metabolic mechanisms of MBSWTSs, the optimization of process performance and waste resource utilization, providing a theoretical and practical foundation for the practical application of MBSWTSs.

Amplification of femtosecond pulses with AI-assisted spectral phase modulation

We report our investigation on ultrashort laser pulse optimization using an AI algorithm in a system consisting of a mode-locked oscillator, a spectral phase shaper, and a highly nonlinear amplifier. We analyzed the performance of the pulse optimization process as a function of two main parameters: the resolution of spectral phase modulation and the number of agents in the algorithm. We showed that the algorithm could find an optimum phase profile for the seed pulse, which allowed for a reduction of the FWHM of the amplified pulse by 10 fs (from 46 to 36 fs), and significantly reduced the intensity of the side-pulse by a factor of 4.6. Importantly, the algorithm used does not require any training and optimizes the pulse shape without any knowledge about the input pulse parameters or the parameters of the amplifier. We believe the proposed system might be a convenient test bed for evaluating various AI-based algorithms in a pulse optimization task.

Simulation and Optimization of Surface Roughness and Process Performance during Machining of HSS by Micro-WEDM Technology.

When machining high-speed steels (HSS) with micro-wire electrical discharge machining (micro-WEDM), high surface quality is achieved as standard. The value of the roughness parameter Ra is less than 0.2 μm. However, the problem is the performance of the electroerosion process (MRR), which is low. This problem is related to the mechanical and physical properties of the HSS in combination with the setting of the main technological parameters (MTP). The proposed solution to eliminate this problem relies on the selection of proper procedures for the determination of optimization criteria in relation to Ra and MTP, with the inclusion of properties of the machined material. The solution consisted in the identification of four significant physical (ρ, κ) and mechanical (Rm, HRC) indicators of HSS properties, on the basis of which a suitable combination of the process output parameters Ra and MRR can be determined through established mathematical regression models using simulation and optimization. In the next step, the proper values of the MTP output process parameter settings, which correspond to the optimized output parameters Ra and MRR during machining of HSS by micro-WEDM technology, were then obtained by the same approach.

A CASE STUDY: INTELLIGENT SHADING RETROFIT TO EXISTING HOME-OFFICE USING MULTI-OBJECTIVE OPTIMIZATION

ABSTRACT Improved energy performance and occupant comfort are driving building design decisions due to the increasing demand for sustainable and green buildings. However, despite the variety of technological developments in other fields, the range of solutions to improve building performance is limited. One of the main limitations for an early designer is a performance evaluation method to facilitate the design process. This paper offers a new shading performance optimization process that can help designers evaluate both daylighting and energy performance and generate optimized and flexible designs that can be further improved by implementing user-specific automation. The proposed performance optimization method utilizes parametric design, building simulation models, and Genetic Algorithms. Common shading design systems are explored through parametric design, and daylighting and energy modeling simulations are performed to evaluate shading device performance. Genetic Algorithms are used to identify design options with optimal energy and daylighting performance. A case study is conducted to verify the effectiveness of the overall process. Results are used to analyze the influence of design decisions among different shading designs. Finally, future directions in both shading design and energy optimization are presented.

Unsupervised Learning Approach for Anomaly Detection in Industrial Control Systems

Industrial control systems (ICSs) play a crucial role in managing and monitoring critical processes across various industries, such as manufacturing, energy, and water treatment. The connection of equipment from various manufacturers, complex communication methods, and the need for the continuity of operations in a limited environment make it difficult to detect system anomalies. Traditional approaches that rely on supervised machine learning require time and expertise due to the need for labeled datasets. This study suggests an alternative approach to identifying anomalous behavior within ICSs by means of unsupervised machine learning. The approach employs unsupervised machine learning to identify anomalous behavior within ICSs. This study shows that unsupervised learning algorithms can effectively detect and classify anomalous behavior without the need for pre-labeled data using a composite autoencoder model. Based on a dataset that utilizes HIL-augmented ICSs (HAIs), this study shows that the model is capable of accurately identifying important data characteristics and detecting anomalous patterns related to both value and time. Intentional error data injection experiments could potentially be used to validate the model’s robustness in real-time monitoring and industrial process performance optimization. As a result, this approach can improve system reliability and operational efficiency, which can establish a foundation for safe and sustainable ICS operations.

Business process engineering, performance optimization and ethics: a mathematical model for decisions

Business process engineering, performance optimization and ethics: a mathematical model for decisions

DED Joining Process and Ultrasonic-Assisted Performance Optimization of High-Strength Aluminum Alloys Manufactured by SLM

DED Joining Process and Ultrasonic-Assisted Performance Optimization of High-Strength Aluminum Alloys Manufactured by SLM

Monitoring biofilm thickness using the membrane aerated biofilm reactor (MABR) fingerprint soft sensor to optimize nitrogen removal.

The ongoing commercialization and installation of full-scale membrane aerated biofilm reactors (MABRs) stimulate the increasing need to monitor biofilm development. Biofilm thickness in MABRs can be assessed indirectly by plotting the exhaust oxygen purity versus bulk ammonia concentration, defined here as the MABR fingerprint soft sensor. Dynamic simulations with diurnal flow variations of an MABR unit model were implemented over a broad range of biofilm thicknesses and influent conditions consisting of variable C/N ratios and applied ammonia fluxes to assess the utility of the MABR fingerprint. Results show that the continuously decreasing trend of the MABR fingerprint plot slopes can be employed as a useful signal for biofilm thickness control in nitrogen removal processes. This technique is useful in a wide range of influent conditions and is helpful for MABR operators and designers to arrange biofilm thickness control events efficiently and determine where in an overall treatment process the technique can be applied to control biofilm thickness and optimize process performance. PRACTITIONER POINTS: The linear relationship between exhaust oxygen purity and bulk ammonia concentration is defined as the MABR fingerprint plot. MABR fingerprint plots are generated for a given biofilm thickness with diurnal flow or short-term loading variations implemented. Continuously decreasing trends of the MABR fingerprint plot slopes are useful signals for biofilm control in nitrogen removal. The MABR fingerprint is useful over a wide range of influent conditions regarding C/N ratios and applied ammonia fluxes. MABR practitioners can use the fingerprint plots to determine when biofilm control measures should be taken.

Multi-planner validation of RapidPlan knowledge-based model for volumetric modulated arc therapy in prostate cancer.

To investigate the performance of a model-based optimization process for volumetric modulated arc therapy (VMAT) applied to prostate cancer patients with the multi-planner. The 120 prostate plans for VMAT treatment were entered into the database system of the RapidPlan (RP) knowledge-based treatment planning. The treatment planning data for each plan was used to create and train the RP model. Twelve prostate cancer cases were selected and were used for planning by a manual of 12 planners based on the clinical protocol for dose constraints. Then, the treatment plans for each patient were compared with the RP model plans and analyzed with Wilcoxon tests. On average, the RP models can estimate comparable doses among all planner plans and clinical plans for the PTV, which Dmax , D95% , D98% , HI, and CI were used to evaluate. For the normal organ doses of the bladder, rectum, penile bulb, and femoral head, all RP model plans showed comparable or better dose sparing than all planner plans and clinical plans. Moreover, the average planning time of the RP model was faster than manual plans by about two times. The RP model can significantly reduce the variation dose of the normal organs compared with the manual plans among the planners. The automated plans of the RP model might benefit from further fine-tuning of the dose constraints of the normal organs, although both procedure plans are acceptable and fulfill the clinical protocol goals so that the RP model can enhance the efficacy and quality of plans.

Optimization of catalytic wet air oxidation process in microchannel reactor for TBBS wastewater treatment

ABSTRACT Catalytic wet air oxidation (CWAO) process is employed for the treatment of N-tert-butyl-2-benzothiazolesulfenamide (TBBS) wastewater in a microchannel reactor that enables continuous operation of the reaction and allows for thorough mixing of oxygen and pollutants. To achieve the optimal process performance, four key parameters of pressure, temperature, time, and the mass ratio of input oxygen to wastewater COD are optimized using both response surface methodology (RSM) and backpropagation artificial neural network (BP-ANN). According to the correlation coefficients of model results and experimental data, BP-ANN performs better than RSM in simulation and prediction. The analysis of variance in RSM shows that all parameters are significant for the obtained quadratic model, but their interactions with each other are not significant. Connection weights algorithm is used to determine the relative importance of these parameters for the process efficiency, and it is demonstrated that temperature is the most influential parameter with a relative importance of 35.61%, followed by pressure (29.74%), time (19.53%) and ROC (15.12%).

On-Site Pilot-Scale Microalgae Cultivation Using Industrial Wastewater for Bioenergy Production: A Case Study towards Circular Bioeconomy.

This case study assesses the valorization of industrial wastewater streams for bioenergy generation in an industrial munition facility. On-site pilot-scale demonstrations were performed to investigate the feasibility of algal growth in the target wastewater on a larger outdoor scale. An exploratory field study followed by an optimized one were carried out using two 1000 L open raceway ponds deployed within a greenhouse at an industrial munition facility. An online system allowed for constant monitoring of operational parameters such as temperature, pH, light intensity, and dissolved oxygen within the ponds. The original algal seed evolved into an open-air resilient consortium of green microalgae and cyanobacteria that were identified and characterized successfully. Weekly measurements of the level of nutrients in pond liquors were performed along with the determination of the algal biomass to quantitatively evaluate growth yields. After harvesting algae from the ponds, the biomass was concentrated and evaluated for oil content and biochemical methane potential (BMP) to provide an estimate of the algae-based energy production. Additionally, the correlation among biomass, culturing conditions, oil content, and BMP was evaluated. The higher average areal biomass productivity achieved during the summer months was 23.9 ± 0.9 g/m2d, with a BMP of 350 scc/gVS. An oil content of 22 wt.% was observed during operation under low nitrogen loads. Furthermore, a technoeconomic analysis and life cycle assessment demonstrated the viability of the proposed wastewater valorization scenario and aided in optimizing process performance towards further scale-up.

Toward a modeling, optimization, and predictive control framework for fed-batch metabolic cybergenetics.

Biotechnology offers many opportunities for the sustainable manufacturing of valuable products. The toolbox to optimize bioprocesses includes extracellular process elements such as the bioreactor design and mode of operation, medium formulation, culture conditions, feeding rates, and so on. However, these elements are frequently insufficient for achieving optimal process performance or precise product composition. One can use metabolic and genetic engineering methods for optimization at the intracellular level. Nevertheless, those are often of static nature, failing when applied to dynamic processes or if disturbances occur. Furthermore, many bioprocesses are optimized empirically and implemented with little-to-no feedback control to counteract disturbances. The concept of cybergenetics has opened new possibilities to optimize bioprocesses by enabling online modulation of the gene expression of metabolism-relevant proteins via external inputs (e.g., light intensity in optogenetics). Here, we fuse cybergenetics with model-based optimization and predictive control for optimizing dynamic bioprocesses. To do so, we propose to use dynamic constraint-based models that integrate the dynamics of metabolic reactions, resource allocation, and inducible gene expression. We formulate a model-based optimal control problem to find the optimal process inputs. Furthermore, we propose using model predictive control to address uncertainties via online feedback. We focus on fed-batch processes, where the substrate feeding rate is an additional optimization variable. As a simulation example, we show the optogenetic control of the ATPase enzyme complex for dynamic modulation of enforced ATP wasting to adjust product yield and productivity.

Integrated Selection of Coformers, Solvents, and Operating Conditions for Optimal Process and Product Performance of Pharmaceutical Cocrystals

Integrated Selection of Coformers, Solvents, and Operating Conditions for Optimal Process and Product Performance of Pharmaceutical Cocrystals

Reversible Data Hiding With Pattern Adaptive Prediction

Abstract In the area of reversible data hiding (RDH), one of the most popular techniques is prediction-error expansion (PEE), which hides data in the prediction errors with well-preserved image fidelity. The key to a successful PEE-based RDH implementation usually lies in prediction algorithms with high accuracy. Existing PEE-based RDH works often employ one single prediction algorithm, which is usually globally optimized, but with less consideration of the pixel distribution characteristics within local neighborhoods. In this manuscript, the technique of pattern adaptive prediction is proposed for pixel estimation according to the type of local binary pattern (LBP), which is obtained from the pixel’s eight neighborhood. Theoretically speaking, pattern-based predictors can be designed for each and every LBP patterns to create multiple prediction-error histograms (PEHs). However, the process of performance optimization with multiple PEHs requires extremely heavy computing power. To speed up the optimization process, LBP patterns are classified into various groups based on the degree of histogram concentration. Experiments demonstrate that the prediction accuracy is obviously improved and the image fidelity is well preserved.

Optimization method of high parameter spiral groove mechanical seal based on axiomatic design theory

At present, in order to improve the performance of spiral groove mechanical seals, the structural parameters of seals are mostly adjusted based on the experience of designers. The integration degree of existing mechanical seal performance research examples is low, and the whole performance optimization process lacks of scientific theoretical framework to guide. In order to solve this problem, combining the axiomatic design theory with the existing mechanical seal performance research, a structural optimization method of spiral groove mechanical seal based on axiomatic design theory was put forward. This method defines the mapping relationship between the three-level functional requirement domain and the design parameter domain of the spiral groove mechanical seal, and thus develops 14×14 diagonal optimization matrix. The research results show that the introduction of axiomatic design provides a basic theoretical framework for the performance optimization of spiral groove mechanical seals and decomposes the optimization process into several design modules, which improves the efficiency and accuracy of the optimization design of spiral groove mechanical seals and has good application value.