Process Optimization Research Articles

Enhancing Oxygen-Dissolving Capacity of Rotary Drum Food Waste Composting: Tumbling Process Optimization and Experimental Validation with Discrete and Finite Element Methods

An optimized tumbling process can significantly improve the oxygen dissolving capacity of composting and fertilizer quality: by increasing the fluffiness of the lower layer of the pile, localized anaerobic fermentation can be avoided, thereby enhancing compost quality. This paper presents a method for improving the oxygen dissolving capacity of rotary drum food waste composting through a combination of simulation optimization and experimental validation. First, the discrete element method was used to optimize the key parameters of the tumbling process. The response surface method was then employed to analyze the composting test results and determine the optimal conditions. To ensure the reliability of the equipment under this method, failure risk analysis was conducted using the finite element method. The simulation optimization results showed that with a rotary drum reactor speed of 3.5 r/min, a horizontal angle of inclination of 2.5°, a mixing blade angle of inclination of 43°, and a blade pitch of 580 mm, the fluffiness of the lower layer of the pile increased by 8.515%, achieving the best tumbling and indirectly enhancing oxygen dissolving capacity. The maximum deformation of the load-bearing components was only 0.0548 mm, and the minimum safety factor was 4.408 (≥1 is considered safe). A 14-day composting experiment was conducted to validate the optimized parameters. The results showed that the maximum temperature of the compost pile reached 68.34 °C (lasting 7 days), with the pH, moisture content, C/N ratio, humus substances, humic acid, and fulvic acid contents of the fertilizer all meeting or exceeding the levels recommended by Chinese national standards. These findings indicate that the optimized tumbling device effectively improved the stability and dissolved oxygen efficiency of food waste composting, providing valuable practical insights and a research foundation for enhancing oxygen efficiency in the composting of other organic wastes.

Digital twinning of vertical centrifugal casting

This paper explores the transformative potential of digital twin technology in vertical centrifugal casting (VCC), a cornerstone manufacturing process for high-integrity cylindrical components. By integrating real-time data, physical models, and machine learning algorithms, digital twins unlock a new paradigm of process optimization, predictive maintenance, and quality control. Integration of digital twinning enhances the performance in different domains of work like enhancing the quality of research, production etc. Howbeit, digital twinning is nascent in the domain of manufacturing specially in the casting sub domain. A physical set up of VCC is integrated with Internet of Things (IoT) and data acquisition system to stream the collected data to the cloud-based server. Transformation of Internet of Things (IoT) enabled VCC integrated with different sensors into the digital twin helps in quality prognosis for future applications. The data is further fetched from the cloud and interconnection is established between the digital twin. Real time monitoring, controlling and operating can be done easily with the help of a digital twin to predict the quality and tentative defect locations. Further amplifying these benefits, emerging technologies like Virtual Reality (VR), Augmented Reality (AR), and the Metaverse hold immense promise for revolutionising VCC training, collaboration, and visualisation.

Optimization of sewage treatment processes: Process control based on artificial intelligence

Abstract. The optimization of sewage treatment processes is critical for improving efficiency and reducing energy consumption. This paper explores the application of machine learning and artificial intelligence algorithms in optimizing key processes such as aeration, sedimentation, and filtration. By leveraging real-time monitoring and adaptive control, these algorithms can dynamically adjust operational parameters to enhance treatment efficiency and minimize energy usage. This study provides detailed insights into the implementation and benefits of AI-driven process control in sewage treatment, supported by case studies and data analysis. The findings indicate significant improvements in treatment performance, showcasing the transformative potential of AI in environmental engineering.

Numerical model of a top-blown rotary converter preheating and charge heating with an oxy-fuel burner

Background The present work is conducted in the framework of the SisAl Pilot EU project, which aims to optimize silicon production in Europe by recycling materials and using carbon-emission-friendly technology. Silicon production experiments were conducted on laboratory and pilot scales in different types of furnaces, including top-blown rotary converters (TBRC) used as chemical reactors for molten slag-metal mixtures. In addition to experimental work, process optimization also relies on numerical modelling. Methods In this study, COMSOL Multiphysics® was used for the numerical testing of a new thermal design of TBRC by simulating its preheating and charge heating owing to an external heat source provided by an oxy-fuel burner. Results and conclusions The risk of slag solidification in TBRC during the aluminothermic reduction of silica was assessed. The model predicts that, with a useful burner power of 600 kW, the empty TBRC can be preheated to 1650°C in less than 30 min. Based on this model, the optimum burner power for maintaining the TBRC charge in a liquid state was determined. The influence of the TBRC inclination angle and its rotation frequency was studied numerically.

Optimization of Planar Flow Melt Spinning Process for Producing Thin, Wide, and Continuous Amorphous Ribbons of Fe73.5Si13.5B9Nb3Cu1 Alloys

Optimization of Planar Flow Melt Spinning Process for Producing Thin, Wide, and Continuous Amorphous Ribbons of Fe73.5Si13.5B9Nb3Cu1 Alloys

Trends and innovations in mycorrhizal biofertilizers: a technological prospecting study

Exponential population growth, combined with the reduction of arable land, has created a need for increased agricultural production within a smaller physical space and with limited water and nutrient resources. In this context, biofertilizers have emerged as an environmentally friendly alternative to meet the demand for technologies and products that enhance productivity. The objective of this study was to map the production of technological knowledge and identify the main characteristics of innovation assets developed in the field of biofertilizers, with a particular focus on mycorrhizal biofertilizers, during the first decades of the 21st century. To achieve this, data was collected from international databases, including the European Patent Office (EPO) and the World Intellectual Property Organization (WIPO), as well as from the national database of the National Institute of Industrial Property (INPI) in Brazil. The search was conducted using simple and combined queries with the keywords "Mycorrhiza" and "Biofertilizer," followed by quantitative and qualitative analyses. The results indicated that the primary patent applicants in this field are companies and universities, predominantly located in North America and Asia. The highest concentration of patent publications occurred between 2010 and 2021, particularly in relation to new biofertilizer compositions and the optimization of production processes for these compounds. Although new technologies in the field of mycorrhizal biofertilizers can be observed, there are still unexplored avenues, and new initiatives will be important for the emergence of innovative solutions.

Process optimization of vacuum concentration of Karonda fruit juice using response surface methodology: effects on antioxidant activity, iron content and GCMS profile

In the realm of fruit processing, karonda juice stands out for its elevated moisture levels, posing challenges in storage and transportation. This study presents a pioneer effort for concentration of karonda juice through optimizing the process parameters viz., temperature and time using central composite design (CCD) of response surface methodology (RSM) within rotary evaporator setup. Through ten experimental runs, variations were introduced, adjusting temperatures from 45 to 55 °C and duration from 90 to 210 min. The results showcased the efficacy of vacuum concentration, reducing moisture content to 16.43% and significantly elevating total soluble solids (TSS) from 9.02 to 89˚B. Moreover, key nutrients experienced substantial increase: ascorbic acid surged from 3.79 to 14.66 mg per 100 g, total phenolic content (TPC) soared from 100.74 to 386.97 mg GAE per 100 g, total antioxidant activity (mg AAE/100 g) escalated from 76.74 to 328.10 mg AAE per 100 g, anthocyanin content (mg/100 g) increased from 10.9 to 129.34 mg per 100 g, and iron content rose from 39 to 150.54 mg per 100 g. GC–MS profile elucidated compounds like Tetrahydrofuran, 3-Hydroxy-3-methylvaleric acid, 3-Hexen-2-one, Hydroperoxide and Octane. The optimization process, guided by RSM revealed the ideal parameters: 55 °C for 150 min, marking a significant advancement in karonda concentration techniques.

Plastic Injection Molding Process Analysis: Data Integration and Modeling for Improved Production Efficiency

This paper presents a comprehensive analysis of the plastic injection molding process through the integration of data acquisition technologies and classification models. In collaboration with a company specializing in plastic injection, data were extracted directly from the machine during a specific period at the beginning of a shift change. These data were subjected to exploratory analysis to identify correlations between important variables, such as injection time, cycle time, and mold pressures. Additionally, classification models, including Random Forest and Logistic Regression, were constructed to predict and classify the process state based on these variables. The model results demonstrated high predictive performance, with 99.5% accuracy for Random Forest and 97% for Logistic Regression. These results provide a strong foundation for the early identification of potential problems and informed decision making to improve the efficiency of the plastic injection molding process. This study contributes to the advancement of the integration of intelligent technologies in industrial process optimization, aligned with the principles of Industry 4.0.

Optimisation of nanofiltration process for pharmaceutically active compounds: Removal of paracetamol, diclofenac and metoprolol

Optimisation of nanofiltration process for pharmaceutically active compounds: Removal of paracetamol, diclofenac and metoprolol

Enterprise financial optimization of precision mechanical manufacturing process based on intelligent materials and thermal environment sensors

Enterprise financial optimization of precision mechanical manufacturing process based on intelligent materials and thermal environment sensors

Optimisation of the Ethanol Fermentation Process Using Hydrothermal Pretreatment of Cellulose Waste—Effect of Fermentation Pattern and Strain

Optimisation of the Ethanol Fermentation Process Using Hydrothermal Pretreatment of Cellulose Waste—Effect of Fermentation Pattern and Strain

Multi-objective Optimization in EDM Process Using Backpropagation Neural Network-Genetic Algorithm (BPNN-GA)

Multi-objective Optimization in EDM Process Using Backpropagation Neural Network-Genetic Algorithm (BPNN-GA)

Multiobjective Optimisation of Flotation Variables Using Controlled-NSGA-II and Paretosearch

Finding the optimum operating points for the maximisation of flotation recovery and concentrate grade can be a very difficult task, owing to the inverse relationship that exists between these two key performance indicators. For this reason, techniques that can accurately find the trade-off are critical for flotation process optimisation. This work extracted well-assessed Gaussian process predictive functions as objective functions for a comparative multiobjective optimisation study using the paretosearch algorithm (PA) and the controlled elitist non-dominated sorting genetic algorithm (controlled-NSGA-II). The main aim was the concomitant maximisation of the copper recovery and the concentrate grade. Comparison of the two applied techniques revealed that the PA discovered the best set of the pareto-optimal solution for both the recovery (93.4%) and concentrate-grade (17.4 wt.%) maximisation.

Multi-Criteria Optimization of the Paper Production Process Using Numerical Taxonomy Methods: A Necessary Condition for Predicting Heat and Electricity Output in a Combined Heat and Power (CHP) System

The subject of this study is the optimization of the paper production process in one of Poland’s leading paper mills. In addition to its primary objective of paper production, the company generates heat and electricity for internal consumption and external clients, including the local municipality. Surplus energy may be sold on the power exchange; however, this requires forecasting the quantity of energy to be sold 24 h in advance, which introduces an element of uncertainty. Production stoppages, often caused by random events such as paper breakage, force a power decrease in the CHP system, further complicating energy forecasting. To minimize the occurrence of such events, numerical taxonomy methods were employed to determine the optimal screen speed (Vs) and winding speed (Vn) for two paper machines, based on the type and weight of the paper produced. This analysis utilized detailed daily data collected by the company over the period 2015–2020. The findings contribute to minimizing the occurrence of paper roll tearing, thereby reducing the risk of inaccurate forecasts of the energy and heat produced by the CHP system. Furthermore, the methodology employed in this study may be effectively applied to other optimization problems in industrial processes.

Artificial neural network in optimization of bioactive compound extraction: recent trends and performance comparison with response surface methodology.

Plant products and its by-products are rich source of bioactive compounds like antioxidants, flavonoids, phenolics, pigments and phytochemicals. Bioactive compound's health-promoting properties are well studied. However, optimal extraction of bioactive compounds is a complex, labour- and time-intensive process. It is also highly sensitive to experimental variables. Predicting output variables can reduce the experimental work and has positive environmental impact. Various tools such as Response Surface Methodology (RSM), Mathematical modelling have been commonly used for optimization and predictive modelling of the extraction process. Although mathematical modelling and RSM are efficient, recent studies have used Artificial Neural Network (ANN) which is more efficient and accurate and can perform extensive predictions with high accuracy. The manuscript focuses on current trends of ANN application in optimizing the extraction of bioactive compounds. In this study, ANN and RSM have been compared in terms of their performances in optimizing and modelling the extraction of bioactive compounds from herbs, medicinal plants, fruit, vegetables, and their by-products. The findings from the literature indicate that efficiency of ANN was superior to RSM. Future researches can focus on use of ANN in industrial optimization experiments.

Qualitative Analysis of the Heat Transfer in a Package of Square Steel Sections

During the heat treatment of square or rectangular steel sections, a heated charge, arranged in regular packages, is placed inside a furnace. This type of charge forms a porous medium through which a complex heat flow occurs during heating. Several heat transfer mechanisms act simultaneously within this medium: conduction through the section walls, conduction and natural convection within the gas, thermal radiation between the section walls, and complex heat transfer (mainly contact conduction) at the joints between the adjacent sections. This article presents a qualitative analysis of heat transfer, aiming to determine the contribution of individual heat transfer mechanisms to the overall process. For this purpose, an analytical model of complex heat transfer within the package was employed, based on the thermo-electric analogy. The results from experimental studies were used to calculate the natural convection and heat transfer at the joints. It was assumed that the material of the sections was low-carbon steel, and the gas was air. Calculations were performed for the temperature range of 25 °C to 700 °C, considering three different geometrical configurations of the sections. It was shown that the effective thermal conductivity (ETC) of the package for the considered geometrical cases varies between 2.2 and 10.6 W/(m·K), which is an order of magnitude lower than the thermal conductivity of the individual sections. This parameter increased dynamically with the temperature. Moreover, the heat transfer intensity within the package of sections was nearly an order of magnitude lower than the heat conduction observed in a solid steel charge. Additionally, it was shown that the primary heat transfer mechanisms governing the heating process were thermal conduction (in the lower temperature range—up to approximately 350 °C) and thermal radiation (in the higher temperature range—above 350 °C). The gas convection inside the sections had a minimal impact on the heating process of the package. The primary parameters influencing the quality of the results were the joint resistance between the adjacent sections and the emissivity of the sections. The presented model can be used for the optimization of heat treatment processes for the considered charge.

Optimization of Vacuum Freeze-Drying Process and Quality Evaluation of Stropharia rugosoannulata

Stropharia rugosoannulata is a valuable medicinal and food fungus with high nutritive value. Freeze-drying addresses the storage and transportation challenges of fresh Stropharia rugosoannulata, expanding its market while preserving its flavor and quality more effectively than other drying methods. This study optimizes the vacuum freeze-drying process for Stropharia rugosoannulata using an orthogonal experiment method. The process parameters were optimized to determine their effects on the quality of the vacuum freeze-dried product, including pre-freezing temperature, pre-freezing time, and freeze-drying time. The optimal conditions were identified as a pre-freezing time of 60 h, a pre-freezing temperature of −80 °C, and a freeze-drying time of 72 h. The optimal product exhibited a bright color close to its natural state, with minimal browning and its natural white color maintained post-drying. During the drying process, the internal structure of the raw materials remained intact. After drying, the finished product retained its natural form, making it suitable for sale on the market. The soluble protein content of the vacuum freeze-dried Stropharia rugosoannulata reached 68 mg/g. Optimizing the freeze-drying process can better preserve the tissue structure and bioactive substances of Stropharia rugosoannulata, providing a reference for high-quality food processing and showing potential for sustainable development.

Intelligent optimization of cold radial forging process for 20CrMnTiH alloy based on GA-BP and performance analysis

Intelligent optimization of cold radial forging process for 20CrMnTiH alloy based on GA-BP and performance analysis

Investigation of roll forming process and quality control factors for metal bipolar plates

Metal bipolar plates (BPPs) are crucial components of proton exchange membrane fuel cells (PEMFCs), with the quality of their formation affects the power generation efficiency and lifespan of the fuel cells. The predominant manufacturing method, stamping, often results in localized excessive stretching of metal BPPs, thereby limiting the depth of the flow channels. Roll forming, dominated by bending deformation, is a forming process that ensures uniform thinning and greater depth of BPPs. The multi-directional flow channel roll forming model for BPPs is constructed in this paper for the first time, the characteristics of the roll forming process is investigated. Effects of process parameters on the forming results are discussed. The investigation indicates that during the roll forming process, the channel ports and corners are critical areas where defects are most likely to occur. The average filling ratio of the longitudinal flow channels is 5.6% higher than that of the transverse flow channels. The plastic strain in the longitudinal flow channels is about 36% higher than in the transverse ones, which making the form is more difficult to form, but the thickness is more uniform. Increasing the diameter of the rollers can enhance the filling ratio and flatness, but it will exacerbate thinning at the corners of the flow channels. When the ratio of the flow channel width to the rib width is close to 1, the filling ratio of the BPP is higher, with less thinning, resulting in a better forming result. As the depth of the flow channel increases, resulting in a thinner BPP, when depth-to-width ratio exceeding 0.6 is more likely to lead to failure. Increasing the speed of roll forming can lead to a greater thinning of the BPP and may even cause it to crack. The thinner the sheet metal is, the higher the filling ratio of the BPP and the more uniform its thickness. Those results can provide technical references for the structural design and forming process optimization of BPPs.

Process development and optimization of sustainable and integrated large scale ammonia production process using water electrolysis based green hydrogen: Investigating process, energy, economic & environmental perspectives

Ammonia is an important raw material for fertilizer production and significantly contributes globally to agricultural systems. The multi-objective optimization of a large-scale integrated ammonia production process using water electrolysis-based green hydrogen is carried out using the Hybrid Multi-Objective Differential Evolution-Distance Learning Scheme (Hybrid MODE-DLS) algorithm. Three optimization cases are solved with the objectives of ammonia production rate, total production cost, and total energy by considering nine decision variables, and multiple constraints. The maximum ammonia production (66.51 kmol/h) and minimum total production cost (105.07 $/yr × 105) are obtained in Case 1 whereas the minimum energy value (7863.45 kWh) is obtained in Case 2. The minimum heat credit value of 3057300.5 $/annum exists in Case 2 solutions whereas the maximum heat credit value of 7992076 $/annum is seen in Case 3. The ammonia production rate increased by 82.79% in Case 2 results compared to the base case results.