Parameter Optimization Technique Research Articles

Thermodynamic simulation study on compression ignition engine operating with oil obtained via catalytic pyrolysis of waste polythene covers

In recent years, waste-to-energy systems are receiving more attention due to their dual benefit of reduction of pollution caused by waste materials and reducing the overconsumption of fossil fuels. Since plastic waste is readily available and has a high heating value, it is a viable source of energy.; plastic garbage makes an excellent source of energy. Today, a feedstock made up of several types of plastic is used to quickly pyrolyze and produce plastic pyrolysis oil (PPO) or waste plastic oil (WPO). The oil could be utilized in internal combustion engines to produce heat and power. In the current work, waste polythene covers were transformed into value added products by utilizing fly ash catalyst and oil obtained is examined for suitability to run in the diesel engine. In the primary phase simulation study is performed on a four-stroke single cylinder DI Compression ignition engine using Diesel-RK engine simulator, results indicated that, waste plastic oil was found to emit higher oxides of nitrogen compared to diesel therefore in the secondary phase the same engine simulated by implementing exhaust gas recirculation at 3.5 %,7 %,10.5 % and 14 % rates. In the end engine parameters get optimized using 3D parametric optimization technique.Simulation Results indicate that, performance parameters such as brake power, brake thermal efficiency and brake specific fuel consumption were found to be decreased slightly under EGR conditions. Furthermore, combustion parameters such as cylinder peak pressure and peak temperature decrease up on implementing the exhaust gas recirculation, similarly the emission characteristics such as CO2, PM and smoke found to be increase with increase in EGR ratio while oxides of nitrogen emissions significantly decreased under EGR conditions. In order to reduce the emissions further, a 3D parametric optimization technique was adopted and optimal results obtained when engine runs at compression ratio of 16.5, injection timing of 19.38 and EGR percentage of 4.8 %.

Modeling and Calibration of Gaia, Hipparcos, and Tycho-2 Astrometric Data for the Detection of Dark Companions

Hidden within the Gaia satellite’s multiple data releases lies a valuable cache of dark companions. To facilitate the efficient and reliable detection of these companions via combined analyses involving the Gaia, Hipparcos, and Tycho-2 catalogs, we introduce an astrometric modeling framework. This method incorporates analytical least-square minimization and nonlinear parameter optimization techniques to a set of common calibration sources across the different space-based astrometric catalogs. This enables us to discern the error inflation, astrometric jitter, differential parallax zero-points, and frame rotation of various catalogs relative to Gaia Data Release 3 (DR3). Our findings yield the most precise Gaia DR2 calibration parameters to date, revealing notable dependencies on magnitude and color. Intriguingly, we identify submilliarcsecond frame rotation between Gaia DR1 and DR3, along with an estimated astrometric jitter of 2.16 mas for the revised Hipparcos catalog. In a thorough comparative analysis with previous studies, we offer recommendations on calibrating and utilizing different catalogs for companion detection. Furthermore, we provide a user-friendly pipeline (https://github.com/ruiyicheng/Download_HIP_Gaia_GOST) for catalog download and bias correction, enhancing accessibility and usability within the scientific community.

Study of the relationships between the technical and economic characteristics of river cargo ships during operational and economic feasibility studies

One of the problems of operational and economic feasibility is determining the economic efficiency of a new ships. The complexity of the problem increases for shipis od river and sea-river class due to the high variability of navigation conditions, which significantly affect efficiency indicators. The purpose of the article is to study the mutual influence of the technical indicators of sea-river cargo ship and the economic indicators of its operation, taking into account the navigation conditions on specific sections of waterways. Various technical indicators are considered and it is shown that the most comprehensive indicator is the specific fuel consumption per ton-kilometer. The influence of the error of forecast technical indicators on economic indicators was assessed. It is shown that the optimality of economic indicators depends on the characteristics of the waterway so that a vessel that is optimal on one waterway may not be optimal on another. It has also been established that optimal technical parameters do not always agree with optimal economic ones.

IMPROVEMENT OF THE METHODOLOGY FOR OPTIMIZING THE PARAMETERS OF COMPLEX SYSTEMS UNDER THE INFLUENCE OF THERMAL LOAD SOURCES

The article investigates some aspects of solving the problem of constructing mathematical models for optimizing the parameters of local laser action on multilayer microbiological systems. The constraints on the desired laser parameters and on the temperature field in the material subject to laser fission are set. Verification of compliance with the restrictions on not exceeding the maximum value of the temperature field requires multiple calculations of the corresponding temperature field in the microbiological material. Control over the temperature field limits ensures the viability of its parts during the biotechnological process of laser fission. To construct adequate optimization mathematical models, the author substantiates the adequacy of the computational mathematical models describing the state of a microbiological system under the action of laser radiation sources. A multi-point boundary value problem with a system of inhomogeneous differential heat conduction equations for a multilayer microbiological medium is investigated and the correctness of this problem is substantiated for minor perturbations of the right-hand side of the differential equation. The results obtained in the article guarantee the adequacy of applied optimization mathematical models for finding rational values of technical parameters of laser emitters. In order to improve the accuracy of optimization of the technical parameters of laser emitters, the article presents a mathematical modeling of the preparatory stage of embryo defrosting. The presented mathematical models and methods of their implementation are necessary to improve the quality of embryo defrosting. Improvement of the methodology for solving applied biotechnological problems will certainly lead to the complication of mathematical models, but it will increase the accuracy of calculation and optimization of technical parameters of the biotechnological process of laser embryo division.

Small-Signal Stability Analysis and Improvement in Multi-Converter Grid-Tied System Based on Gerschgorin Disc Theorem

The integration of a large number of voltage source converters (VSCs) into the power grid decreases the small-signal stability of the power system. When several VSCs with different control parameters are simultaneously connected to the power grid to form a multi-converter grid-tied system, the potential destabilizing factors increase. Thus, parameter optimization for stability-weakest parameters that have the greatest impact on the system stability becomes more significant in addressing small-signal stability issues. This paper first proposes a stability evaluation function based on the Gerschgorin disc theorem, which can assess the stability of the multi-converter grid-tied system. Then a parameter sensitivity method is proposed to identify the stability-weakest parameters. Finally, an iterative calculation-based parameter optimization method is developed to regulate the identified stability-weakest parameters. Hence, the parameter optimization technique in this research can improve the system stability without requiring eigenvalue solutions and has the merit of low computational complexity. Simulation results based on both the MATLAB/Simulink (2023a) and the RT-LAB (OPAL-RT 5700) platform of a multi-converter grid-tied system validate the correctness of the theoretical analysis and the effectiveness of the parameter optimization method.

Predicting the Power Requirement of Agricultural Machinery Using ANN and Regression Models and the Optimization of Parameters Using an ANN–PSO Technique

Optimizing the design and operational parameters for tillage tools is crucial for improved performance. Recently, artificial intelligence approaches, like ANN with learning capabilities, have gained attention for cost-effective and timely problem solving. Soil-bin experiments were conducted and data were used to develop ANN and regression models using gang angle, velocity ratio, soil CI, and depth as input parameters, while tractor equivalent PTO (PTOeq) power was used as an output. Both models were trained with a randomly selected 90% of the data, reserving 10% for testing purposes. In regression, models were iteratively fitted using nonlinear least-squares optimization. The ANN model utilized a multilayer feed-forward network with a backpropagation algorithm. The comparative performance of both models was evaluated in terms of R2 and mean square error (MSE). The ANN model outperformed the regression model in the training, testing, and validation phases. A well-trained ANN model was integrated with the particle-swarm optimization (PSO) technique for optimization of the operational parameters. The optimized configuration featured a 36.6° gang angle, 0.50 MPa CI, 100 mm depth, and 3.90 velocity ratio for a predicted tractor PTOeq power of 3.36 kW against an actual value of 3.45 kW. ANN–PSO predicted the optimal parameters with a variation between the predicted and the actual tractor PTOeq power within ±6.85%.

Optimizing seismic fragility assessments for high-speed railway track bridges: a novel multi-parameter methodology and intensity measure selection

This research innovatively presents a multi-parameter seismic fragility analysis methodology anchored on Blending ensemble learning tailored for the high-speed railway track-bridge (HSRTB) system. Leveraging ensemble machine learning models, a seismic demand model encapsulating multiple earthquake intensity measures (IMs) is formulated. The research progresses to deduce the mean seismic fragility representations for single, dual, and composite earthquake parameters. Key findings of this investigation underscore that the advanced methodology aligns well with the Monte Carlo (MC) fragility, signifying its robustness. Crucially, earthquake IM parameters, Sa(0.5) and Sa(0.3)_ASI, emerged as the optimal choices for singular and dual-parameter seismic fragility delineations. Further enriching this research, a composite parameter optimization technique rooted in multi-CPU genetic algorithms is proposed. This avant-garde method remarkably minimizes the fragility estimation error, emphasizing its unparalleled efficacy in enhancing the precision of fragility evaluations.

Optimization of structural and electrical properties of graphene-based TiO2 thin film device using Bayesian machine-learning approach

The optimization of laser-induced graphene (LIG) patterning is crucial for achieving desirable electronic properties in flexible electronic devices. In this study, we applied state-of-the-art automated parameter optimization techniques, specifically Bayesian optimization, to enhance the electrical resistance of LIG patterns. By iteratively optimizing the laser power, irradiation time, pressure, and gas type, we achieved minimum LIG resistance within eight batch configurations suggested by the Bayesian optimization (BO) approach. Our method eliminates the reliance on skilled operators, as the initial surrogate models were trained with random parameter evaluations. Notably, our system enables the optimization of material properties even when characterizations are only available outside the experiment loop. Furthermore, the surrogate model provided insights into the underlying mechanisms of LIG growth on quartz substrates. Through partial dependence analysis, we identified relevant physical domains for further investigation, leading to the discovery of negative capacitance and a correlation between structural and electrical properties in LIG. These findings were supported by XPS, Raman, and optical characterizations. Our approach streamlines the experimental design, reducing time and cost while accelerating materials research, and offers human-interpretable conclusions for a deeper understanding of LIG patterning processes.

Fabrication of electrospun nanofiber from a blend of PVC and PHB

Abstract In this work, the fabrication process of electrospun nanofibers from a blend of polyhydroxy butyrate (PHB) and polyvinyl chloride (PVC) has been investigated. PVC/PHB nanofibers have been fabricated from solutions using different PVC and PHB ratios. The influence of technical parameters of the electrospinning process on the fabrication, morphology, and diameter of nanofibers has been evaluated. The chemical structure and thermal properties of PVC/PHB have been studied. The results show that the diameter of PVC/PHB nanofibers increases as the PHB content increases. In addition, the optimal technical parameters of the electrospinning process for each PVC and PHB ratio are different. Infrared spectroscopy analysis revealed an enhancement of the crystalline phase of the polymer composite with increasing PHB content. The thermal properties of PVC/PHB nanofibers were evaluated through differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA). The change in PHB ratios leads to a change in the glass transition temperature of PVC/PHB nanofibers. The thermal degradation process of PVC/PHB includes two steps. Increasing the PHB content leads to an enhancement in the mechanical strength of PVC/PHB nanofiber mats; however, it also results in a reduction in tensile elongation. Based on the results of structural, morphological, interaction analysis, and mechanical properties of PVC/PHB nanofibers, this study contributes to the optimization of the fabrication of nanofibers from PVC and PHB. PVC/PHB nanofibers have the potential to be used for air filtration applications.

Key Parameters of Separated Nitrogen Injection in Goaf Based on Orthogonal Test

ABSTRACT Coal spontaneous combustion (CSC) is a common disaster in underground coal mine, and nitrogen injection into goaf is a common means of preventing and extinguishing fire. Conventional single-pipe nitrogen injection in wide working faces has problems, such as narrow inert area, serious nitrogen waste, and poor fire prevention and extinguishment effects. To solve these problems, this study proposes the separated nitrogen injection technology in goaf. Taking Dahaize Coal Mine as an example, a three-dimensional numerical model of multifield coupling in goaf is established. The distribution range of the CSC hazard zone in goaf is determined through field observations and numerical simulations. The validated computational fluid dynamics model is used to determine the optimal nitrogen injection location. Furthermore, the effects of nitrogen injection location in inlet side (NILI), nitrogen injection location in return side (NILR), and nitrogen injection distribution ratio in both sides (NIDR) on goaf inertisation are studied via orthogonal tests and numerical simulations. The optimal technical parameters of the separated nitrogen injection technology are determined by range analysis. The best inertisation effect is obtained when NILI is 30 m, NILR is 30 m, and NIDR is 4/6. Compared to other cases, the CSC hazard zone area and temperature anomaly zone area are significantly smaller, the asphyxiation zone area is significantly greater, and the maximum temperature is slightly lower in this case. This study provides a reference for preventing and controlling CSC in the goaf of underground coal mines.

Development of a parametric optimization concept for the sun control of the outdoor spaces: the case study of 5A business park, Cairo, Egypt

PurposeComfortable outdoor workspaces are important for employees in business parks and urban areas. Prioritizing a pleasant thermal environment is essential for employee productivity, as well as the improvement of outdoor spaces between office buildings to enhance social activities and quality of outdoor workplaces in a hot arid climate has been subjected to very little studies Thus, this study focuses on business parks (BPs) landscape elements. The objective of this study is to enhance the user's thermal comfort in the work environment, especially in the outdoors attached to the administrative and office buildings such as the BPs.Design/methodology/approachThis research follows Four-phases methodology. Phase 1 is the investigation of the literature review including the Concept and consideration of BP urban planning, Achieving outdoor thermal comfort (OTC) and shading elements analysis. Phase 2 is the case study initial analysis targeting for prioritizing zones for shading involves three main methods: social assessment, geometrical assessment and environmental assessment. Phase 3 entails selecting shading elements that are suitable for the zones requiring shading parametrize the selected shading elements. Phase 4 focuses on the optimization of OTC through shading arrangements for the prioritized zones.FindingsShading design is a multidimensional process that requires consideration of various factors, including social aspects, environmental impact and structural integrity. Shading elements in urban areas play a crucial role in mitigating heat stress by effectively shielding surfaces from solar radiation. The integration of parametric design and computational optimization techniques enhances the shading design process by generating a wide range of alternative solutions.Research limitations/implicationsWhile conducting this research, it is important to acknowledge certain limitations that may affect the generalizability and scope of the findings. One significant limitation lies in the use of the shade audit method as a tool to prioritize zones for shading. Although the shade audit approach offers practical benefits for designers compared to using questionnaires, it may have its own inherent biases or may not capture the full complexity of human preferences and needs.Originality/valueFew studies have focused on optimizing the type and location of devices that shade outdoor spaces. As a result, there is no consensus on the workflow that should regulate the design of outdoor shading installations in terms of microclimate and human thermal comfort, therefore testing parametric shading scenarios for open spaces between office buildings to increase the benefit of the outer environment is very important. The study synthesizes OTC strategies by filling the research gap through the implementation of a proper workflow that utilizes parametric thermal comfort.

Optimization Method of Jet Pump Process Parameters and Experimental Study on Optimal Parameter Combinations

The performance of jet pumps depends significantly on their structural and operational parameters. Current research primarily concentrates on theoretical models and laboratory tests, with limited experimental investigations and comprehensive reports concerning jet pump performance under various parameter combinations. In this study, we take a comprehensive approach, integrating both experimental and theoretical methods to assess jet pump performance under optimized process parameters and sand-flushing capabilities. The key findings of this study are as follows: While the characteristic and efficiency equations of jet pumps effectively describe the interaction of critical factors, including the area ratio, pressure ratio, flow rate ratio, and density, they unfortunately do not account for the influence of crucial structural factors, such as the nozzle throat distance, throat length, and diffuser length, on overall performance. Moreover, the parameter optimization technique based on the P-M curve has limitations and requires a tailored design and evaluation to consider the maximum suction capacity in practical engineering contexts. Notably, the nozzle throat distance significantly affects the jet pump suction capacity. Increasing the nozzle throat distance from 6 mm to 12 mm substantially enhances the suction capacity, albeit with a minor reduction in the lifting capacity. Extending the distance from 12 mm to 18 mm initially boosts the suction capacity, followed by a subsequent decline, along with a decrease in the lifting capacity. Interestingly, jet pumps effectively handle sand suction at relatively low pump pressures, creating negative-pressure conditions. As the pump pressure increases, the suction capacity remains relatively stable. However, the lifting capacity increases proportionally with the pump pressure, providing valuable theoretical and technical insights for practical jet pump applications. In summary, our study introduces a comprehensive approach to evaluate jet pumps by integrating experimental and theoretical methods. These insights highlight the intricate relationship between jet pump characteristics and operational parameters, offering essential knowledge for the efficient utilization of jet pumps in various applications.

Boosting the visibility of services in microservice architecture

AbstractMonolithic software architectures are no longer sufficient for the highly complex software-intensive systems, which modern society depends on. Service Oriented Architecture (SOA) surpassed monolithic architecture due to its reusability, platform independency, ease of maintenance, and scalability. Recent SOA implementations made use of cloud-native architectural approaches such as microservice architecture, which has resulted in a new challenge: the discovery difficulties of services. One way to dynamically discover and route traffic to service instances is to use a service discovery tool to locate the Internet Protocol (IP) address and port number of a microservice. In the event that replicated microservice instances are found to provide the same function, it is crucial to select the right microservice that provides the best overall experience for the end-user. Parameters including success rate, efficiency, delay time, and response time play a vital role in establishing a microservice’s Quality of Service (QoS). These assessments can be performed by means of a live health-check service, or, alternatively, by making a prediction of the current state of affairs with the application of machine learning-based approaches. In this research, we evaluate the performance of several classification algorithms for estimating the quality of microservices using the QWS dataset containing traffic data of 2505 microservices. Our research also analyzed the boosting algorithms, namely Gradient Boost, XGBoost, LightGBM, and CatBoost to improve the overall performance. We utilized parameter optimization techniques, namely Grid Search, Random Search, Bayes Search, Halvin Grid Search, and Halvin Random Search to fine-tune the hyperparameters of our classifier models. Experimental results demonstrated that the CatBoost algorithm achieved the highest level of accuracy (90.42%) in predicting microservice quality.

Structural optimization of orthopedic hip implant using parametric and non-parametric optimization techniques.

This research investigates the reduction in weight of hip implant by the application of parametric and non-parametric optimization techniques. Orthopaedic hip implants can be made from metals, ceramics, composites, or metallic alloys and are generally solid structures. The stiffness of orthopaedic hip implant is a pertaining problem when implanted in the human body as Hip implant are stiffer than bone material and causes stress shielding. This results in bone weakening which causes osteoporosis. Reduction in mass of femur stem results in stiffness reduction of femur stem. Non-Parametric topology optimization results in 34.9% mass reduction and parametric optimization based on Central Composite Design technique in Design of Experiments (DoE) uses hole diameters as parameters and performs structural optimization that results in 22% mass reduction.

Stability Constrained Optimal Operation of Standalone DC Microgrids Considering Load and Solar PV Uncertainties

Operation and control dynamics of islanded DC microgrids (DC mGs) have received enormous interest recently in distribution system management. In this regard, research concern on droop-controlled distributed generations (DGs) integrated within the standalone DC mG is becoming more profound. This paper proposes an optimal operation of droop-controlled standalone DC mG with combined small-signal stability and economic-environment-related objectives. The proposed approach optimizes the principal eigenvalue that affects the system stability and two other objectives. An eigen value-based stability analysis for network configurations, with the possibility of its buses solely connected to either dispatchable DG or constant power load (CPL), is proposed. Moreover, uncertainties involved in mG network variables that originate due to the integration of solar PV generation with load consumption were considered and modelled with relevant probability characterization. A recent swarm-based intelligent technique (dragonfly algorithm) for optimization of droop parameters is adopted in the proposed approach. A unique bi/tri-objective combinations are solved, and obtained results are discussed. To show the adaptability of the proposed approach for any given DC mG network, a standalone 6-bus mG was adopted to verify the applicability. Furthermore, the time-domain simulations were carried out on a sample 3-bus DC mG to validate the proposed strategy.

A Modified Polygon Shaped Microstrip Patch Antenna Array for Ultra-Wideband Applications

A single element and two elements array polygon shape microstrip antenna is proposed and analyzed in this paper for ultra-wideband (UWB) applications. The technique used to improve antenna performance is etching rectangular slots on the patch of the antenna with different dimensions and positions. Two Polygon Shape antenna patches are introduced to enhance the antenna properties. Also, multi-slots are reached to improve the antenna gain, impedance matching sweep parameter optimization technique is used to all antenna parameters to enhance antenna properties. The dimensions of the single element antenna are 30 x 33 mm2 and consist of a slotted polygon patch printed on an FR-4 substrate. The proposed antenna has a resonant frequency of 6.2 GHz, while the dimensions of two elements antenna array are 60 x 33 mm2 and the resonance frequency is 9.8 GHz. A technique of inset feed transmission line was utilized to match the 50 Ω microstrip feed line and the radiating patch. These considerations make the structure is suitable for fifth-generation generation (5G) mobile wireless applications. The proposed antenna was simulated by use Computer Simulation Technology (CST-2020) software.

Determination of online thin film buckling configuration by parametric optimization for flexible sensor application

A mini basket type mapping catheter consists of thin film flexible sensors and is applied in the medical field to measure the electrocardiography (ECG) signals in order to localize and quantize the physiological condition/status of heart. The flexible nature of the thin film changes the configuration with respect to the contact boundary conditions when it contacts a target surface. Therefore, to accurately localize the flexible sensor, the thin film flexible sensor’s configuration must be determined accurately in an on-line fashion. As a study of localizing the thin film flexible sensor, this study proposes an on-line thin film buckling configuration determination method using parametric optimization and interpolation technique. With the specific modulus of elasticity and dimensions of the thin film flexible sensor of the mapping catheter prototype, the buckling configuration with two point boundary condition under axial load can be calculated in desktop environment. The proposed calculation method is validated by mapping catheter sensor prototype test. The calculation/test results showed that the maximum overall length L, x_{a}, and y_{a} value error between the calculation and experiment are approximately 0.16 mm, − 0.12 mm. − 0.10 mm in 50 ms calculation time. The calculation result of the proposed method is also compared with that of the numerical simulation by FEM, which has approximately 0.44 mm y_{a} value error compared with that of the experiment.

APPLICATION OF CALCULATION METHODS FOR SOLVING APPLIED PROBLEMS OF HEAT AND MASS EXCHANGE IN COMPLEX SYSTEMS

The accuracy of the calculation and optimization of the objective function and its parameters when solving applied engineering problems depends on the accuracy of the formulation of the main optimization problem, calculation and applied optimization problems, as well as the accuracy of computational methods for their implementation. An increase in the considered features of applied optimization problems will complicate the setting and methods of implementing boundary value problems. Thus, for the implementation of modernized boundary value problems, it will be necessary to apply several computational methods that will create a computational structure. The main condition for constructing a physically based boundary value problem is to find and justify the conditions for the existence of a unique solution. To increase the efficiency of the use of methods of calculation and optimization of technical parameters, it is necessary to increase the number of considered features of calculation and applied optimization mathematical models for heat and mass transfer in technical systems. Along with the construction of boundary value problems, it is important to define and justify the conditions for the existence of a single solution.
 The research article deals with some aspects of solving applied problems of heat and mass transfer in technical systems. Nonlocal boundary value problems for inhomogeneous and homogeneous pseudodifferential equations in partial derivatives with integral boundary conditions are considered, methods of solving a nonlocal inhomogeneous boundary value problem are proposed, and the correctness conditions of this problem in the class of infinitely differentiable generalized functions of power growth are defined and proven. Proved conditions for the existence of a correct problem for pseudo-differential equations with an integral boundary condition. The research of this article should be applied for controlling possible risks when solving applied problems in technical systems, biotechnology and veterinary medicine.

Cost-Effective Processes for Denim Production Wastewater: Dual Criterial Optimization of Techno-Economical Parameters by RSM and Minimization of Energy Consumption of Photo Assisted Fenton Processes via Direct Photovoltaic Solar Panel Integration

Denim production wastewater is an industrial wastewater with a high organic pollutant content. The aim of this study was to improve a cost-effective method via solar panel integration to the photo Fenton process (PFP) and photo electrochemical Fenton process (PEFP) for removing high chemical oxygen demand (COD) from denim production wastewater. To determine process parameter values, the double criterial optimization option was used. The results that maximized the COD removal efficiency and minimized the operating cost of two oxidation processes were determined by response surface methodology (RSM). Optimum operation conditions for the PFP process were 3.18 initial pH, 2.3 g/L Fe2+ concentration, and 27 g/L H2O2 concentration while they were 3.00 initial pH, 27.06 A/m2 current density, and 28.16 g/L H2O2 concentration for PEFP. At the optimum conditions, COD and the total organic carbon (TOC) removal efficiency of PFP were 85% and 61%, respectively. They were determined as 90% and 73% in PEFP. Carbon oxidation state (COS) and average oxidation state (AOS) parameters were used to obtain the biodegradation capability of organic materials. The biodegradability capability of wastewater was observed as high after the Fenton processes. As a result of the optimization of technical parameters, total operating cost was obtained as USD 14.62/m3 (USD 4.25/kgCODremoved) in PFP and USD 13.79/m3 (USD 3.73/kgCODremoved) in PEFP. After the integration of the photovoltaic solar panel to the processes, the total operating cost of PFP and PEFP decreased in a ratio of 61% and 64%, respectively.

Response surface optimisation of technological parameters for producing Ganoderma lucidum by solid-state fermentation from Panax notoginseng residues and kinetics

The present work aimed to investigate the optimal control strategy for Ganoderma lucidum (Chuanzhi No. 6) fermentation from Panax notoginseng residue. Optimisation of the solid-state fermentation (SSF) technical parameters, including inoculum dosage, fermentation temperature, and fermentation time was carried out using the single-factor and Box-Behnken design methods. Results showed that the optimal technical parameters were inoculum dosage of 15.28%, fermentation temperature of 28.42°C, and fermentation time of 14 d. The predicted maximum biomass of G. lucidum was 0.4327 g, which was also verified by validation experiments. The biomass of G. lucidum increased significantly with optimised technological parameters. The logistic equation, the Boltzmann function, and the four-parameter logistic equation were respectively suitable for modelling G. lucidum growth kinetics (R2 = 0.9754), Ganoderma triterpene production kinetics (R2 = 0.9897), and the matrix consumption kinetics (R2 = 0.9826). These models can be used to predict the SSF process of G. lucidum, thus providing a theoretical basis for the development of new G. lucidum drugs, while at the same time recycle Panax notoginseng residue.