Waste Of Resources Research Articles

Photooxidation of Polystyrene into High‐Value Chemicals

Plastics play a crucial role in the survival and advancement of human civilization. However, the increasing challenge of plastic pollution presents significant obstacles. Disposal methods such as incineration and landfilling result in substantial resource wastage and pose potential environmental hazards. The slow progress in recycling and reusing waste plastics hinders their industrial application and effective response to environmental crises. Photocatalysis offers promising solutions for the future of plastic recycling, particularly with the recent increase in photocatalytic degradation methods for polystyrene (PS). This review provides a comprehensive overview of the latest advancements in the photooxidation of PS into high‐value chemicals.

An industrial robot based sawing method for natural stone sculpture

Abstract The advancement of industrial robot technology has opened up a new direction for stone sculpture machining. However, whether through traditional hand carving or modern industrial robotic grinding, a significant amount of natural stone must be removed, typically in the form of stone powder. This process not only decreases machining efficiency but also results in the wastage of stone resources due to the generation of stone powder, which can negatively impact the ecological environment. In order to enhance the efficiency of stone sculpture machining while reducing natural stone waste and environmental pollution, a method for the efficient and green sawing of natural stone sculptures using industrial robots is proposed. First, based on the processing characteristics of diamond wire saw, a mathematical model is developed for the ruled surface generated by the saw's movement and its corresponding compensated surface. Second, with the contour curve of the model as the directrix, an enveloping ruled surface is generated. Next, the minimum volume ruled surface model is generated by Boolean operation using the enveloping ruled surface and the model bounding box. Then, the contour curve corresponding to the minimum volume ruled surface model (the directrix of the envelope ruled surface) is utilized as the machining path for an integrated robotic diamond wire cutting system. A ruled surface model of Stanford Bunny was obtained by sawing a marble block using the robotic diamond wire cutting system. The experimental results show that the material removal rate of the robotic diamond wire cutting is 1.37 times that of saw blade cutting and 2.30 times that of grinding. The volume of stone powder generated during the processing was 0.16 × 10⁷mm³, accounting for only 1.47% of the total stone powder produced during the grinding process. In summary, the proposed method not only enhances processing efficiency but also reduces natural stone waste and mitigates environmental pollution.

Resilient Event‐Triggered Control for Switched Systems With Reconstructed Switching Signals Under DoS Attacks

ABSTRACTIn this article, the resilient event‐triggered control issue for switched linear systems with the bounded disturbance is explored under the aperiodic denial‐of‐service (DoS) attacks. Unlike the discussions on asynchronous behavior caused by network transmission/DoS attacks, configure a copy of predictor at controller module to avert the negative impacts of asynchronous switching. Subsequently, by establishing a resilient event‐triggered strategy (RETS) to eliminate invalid sampling during DoS attacks intervals, the waste of network resources is reduced. Besides, a sufficient condition is derived to ensure the input‐to‐state stability (ISS) for the close‐loop switched systems under DoS attacks. The results indicate that the switched systems can resist a certain degree of DoS attacks under the average dwell time (ADT) constrained by DoS attacks frequency and duration. In addition, Zeno behavior is eliminated by calculating the lower bound of the triggering intervals. Finally, the availability of theoretical approach is verified through a numerical simulation.

Assessing temporal dynamics of nitrogen surplus in Indian agriculture: district scale data from 1966 to 2017

Nitrogen (N) is essential for agricultural productivity, yet its surplus poses significant environmental risks. Currently, over half of applied nitrogen is lost, resulting in resource wastage, contributing to increased greenhouse gas emissions and biodiversity loss. Excess nitrogen persists in the environment, contaminating soil and water bodies for decades. Quantifying detailed historical N-surplus estimation in India remains limited, despite national and global-scaled assessments. Our study develops a district-level dataset of annual agricultural N-surplus from 1966-2017, integrating 12 different estimates to address uncertainties arising from multiple data sources and methodological choices across major elements of the N surplus. This dataset supports flexible spatial aggregation, aiding policymakers in implementing effective nitrogen management strategies in India. In addition, we verified our estimates by comparing them with previous studies. This work underscores the importance of setting realistic nitrogen management targets that account for inherent uncertainties, paving the way for sustainable agricultural practices in India, reducing environmental impacts, and boosting productivity.

Snow Resource Reutilization: Design of Snow Collection and Compression Equipment Based on Functional Analysis Method

High-latitude regions of the Earth are rich in natural snow resources; however, owing to their negative impact on daily life, they have not been effectively utilized for a long time and are instead viewed as obstacles that require substantial resources for clearance. This waste of resources contradicts the principles of sustainable development. With the rapid development of the ice and snow industry, the social and economic value of snow resources is gradually becoming apparent. Therefore, to promote sustainable social and economic development, this study explores new methods for processing snow resources to achieve their recycling and high-value transformation. This study employs the functional analysis method to analyze and solve the functions of the design system, utilizing the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation for the objective assessment and selection of schemes. The obtained relative optimal principles are utilized to guide the parameter setting and innovative design of the scheme in terms of functional structure. After the final scheme is output, its feasibility is verified through finite element simulation. Ultimately, to address the issue of snow resource clearing and recycling, this study designs a product scheme capable of collecting and compressing snow on road surfaces, proposing the use of snow in the form of standardized compressed snow blocks for large-scale snow sculpture construction and other fields. This method significantly reduces the cost of snow sculpture production, enhances efficiency, realizes the comprehensive utilization and high-value transformation of snow resources, and provides a reference for the sustainable development of the low-altitude ice and snow tourism industry.

Physical properties and hydration behaviors of paste casting brick containing coal gasification slag (CGS): Effect of curing condition and CGS dosage

In the process of developing coal chemical technologies, it is necessary to address environmental pollution and resource waste caused by the associated solid waste coal gasification slag (CGS). This study investigated the feasibility of using CGS as a substitute for fly ash (FA) in the preparation of paste casting bricks. The effects of curing conditions and CGS dosages on the physical properties of the specimens were investigated and related to the hydration behaviors through various microscopic techniques. The results showed that the occurrence of electrostatic flocculation and the enrichment of active substances increased the yield stress of the slurry as the CGS replacement rose, which manifested as decreased fluidity at the macroscopic level. The secondary hydration reaction was facilitated with the increased CGS replacement rate due to the desirable hydraulic activity of CGS. XRD, FTIR and TG results corroborated that the content of hydration products which contributed to strength development were enhanced with the addition of CGS under different curing conditions. XRD and SEM-EDS results indicated that the doping of elements such as Mg and Al under autoclaved condition led to a reduction in the grain size of tobermorite, and the micro-morphology tended to concentrate stresses under pressure. The results of this study promote the resourceful utilization of CGS in building materials and lay the foundation for further scale-up industrial application.

Zooming Out: Circular Economy Development in the European Union and its Implications on the Economy and Society

The concept of the circular economy has emerged as a response to the challenges posed by resource constraints, waste accumulation, and pollution, threatening both social welfare and business profitability. The circular economy aims for sustainable development, encompassing environmental quality, economic prosperity, and social equity across current and future generations. Despite its recognition as a pivotal solution, there needs to be more comprehensive understanding regarding its development and its economic and social impacts. Therefore, the objective of this paper is to investigate the progress of circular economy sectors in the European Union to get an overview of the circular economy advancements and discuss geographical economic and social effects. The focus is on the indicators of gross value added, private investments, and full-time employment within recycling, repair, and reuse sectors because these sectors provide good proxies for the circular economy activities in the European economy. Results suggest a linear growth of circular economy sectors across the European Union, albeit with variations among member states. The paper discusses the implications of circular economy transition on the economy and society, particularly on the labour market, skill demands, and technology integration, emphasising the need for a critical academic discourse. It questions assumptions about job creation and local employment, especially considering technological advancements that may reduce manual labour in recycling plants. Future research is invited to focus on these macro-level implications and critically reflect on the societal implications of a circular economy transition to shape a more fair and socially inclusive economy.

A bio-based, self-healable, conductive rubber film with oxidized cellulose nanofiber segregated network

Rubber composites are indispensable in all areas of our daily lives. However, the formation of permanent crosslinked networks in rubber materials makes it difficult to recycle, resulting in a non-negligible waste of resources. In this paper, a vulcanization-free, fully bio-sourced rubber composite was prepared by using oxidized natural rubber (oNR) and oxidized cellulose nanofibers (TOCFs). TOCFs are selectively dispersed between the latex particles to form a segregated network. Meanwhile, the formation of hydrogen-bonding between oxygenated polar groups of oNR and abundant hydroxyl and carboxyl groups of TOCFs improves their interfacial interactions. This special structure promotes strain-induced crystallization (SIC) behavior of oNR matrix, giving its tensile strength up to 14.7 MPa. Furthermore, the oNR/TOCFs film shows excellent self-healing efficiency (96 %) at 40 °C for 5 h. The hygroscopicity of the TOCFs segregated network can turn the oNR/TOCFs film to be a conductive film by regulating the absorbed water content. The film has high conductivity (0.05 S/m) at a water content of 8.99 wt%, and the resistance change (RV/R0) can be varied between 1–5.9 × 10−6 at a water content range of 0–8.99 wt%, which makes it have potential for a wide range of humidity monitoring applications.

Analysis on the Influence law and Mechanism of the Strength of Reclaimed Aggregate-Industrial Waste Slag Solidified Silt

To optimize the usage of construction waste resources, industrial waste slag and silt, this paper used Portland cement, mineral waste residue and phosphogypsum composite to make cementing material (CMPS) with construction waste reclaimed aggregate to solidify silt. The laboratory solidification test and microscopic analysis were conducted to assess the mechanical characteristics of solidified sludge. In order to clarify the mineral composition, microscopic morphology and pore characteristics of the regenerated aggregate and CMPS solidified silt (SS), XRD, SEM and nitrogen adsorption pore analyzer were employed for further explore the regenerated aggregate and CMPS solidified silt effectively, and further reveal the internal mechanism of the regenerated aggregate and CMPS solidified silt effectively. The findings indicated that the strength of Portland cementmineral waste residue phosphogypsum terpolymer system curing agent increased by 107.34% than that of single Port-land cement SS at 56 d, and the strength of CMPS solidified silt increased by 25.68% under the action of reclaimed aggregate framework. The maintenance period and water composition of the silt have high correlation with the strength. Therefore, influence law of above two influencing factors on its mechanical properties were further explored and the strength prediction were made. The microscopic test results showed that, drawing from the hydration of Portland cement and pozzolans reaction of mineral waste residue, the solidified system has pre-pared CaSiO3 hydrate gel and ettringite crystals with gelatinous properties, which help to fill the pores and form a denser structure.

Combating food waste in the agricultural supply chain: A systematic review of supply chain optimization strategies and their sustainability benefits

Food waste represents a significant global challenge, with approximately one-third of all food produced for human consumption lost or wasted annually, leading to severe environmental, economic, and social consequences. The issue not only exacerbates food insecurity but also contributes to greenhouse gas emissions, waste of resources, and economic losses estimated at over $1 trillion each year. This paper aims to systematically review the causes of food waste across agricultural supply chains and explore effective supply chain optimization strategies to mitigate these losses. Through a comprehensive analysis, the paper identifies critical drivers of food waste, including post-harvest losses, inefficient processing, and aesthetic standards, while proposing technological innovations such as the Internet of Things (IoT), blockchain, and artificial intelligence (AI) as solutions to enhance supply chain efficiency. Furthermore, it evaluates policy frameworks that support waste reduction, highlighting the role of government incentives and regulations in promoting sustainable practices. The findings underscore the importance of reducing food waste not only for environmental sustainability but also for economic profitability, as optimizing supply chains can lead to substantial cost savings and increased revenue for businesses. Ultimately, the paper emphasizes the necessity for collaboration among stakeholders—including governments, industries, and non-profit organizations—to create systemic change and effectively combat food waste on a global scale. By fostering innovative practices and policies, we can pave the way for a more sustainable agricultural future, ultimately contributing to the alleviation of food insecurity and the enhancement of economic resilience.

Development of a Hybrid AI Model for Fault Prediction in Rod Pumping System for Petroleum Well Production

Rod pumping systems are widely used in oil wells. Accurate fault prediction could reduce equipment fault rate and has practical significance in improving oilfield production efficiency. This paper analyzed the production journal of rod pumping wells in block X of Xinjiang Oilfield. According to the production journal, oil well maintenance operations are primarily caused by five types of faults: scale, wax, corrosion, fatigue, and wear. These faults make up approximately 90% of all faults. 1354 oil wells in the block that experienced workover operations as a result of the aforementioned factors were chosen as the research objects for this paper. To lower the percentage of data noise, wavelet threshold denoising and variational mode decomposition were used. Based on the bidirectional long short-term memory network, an intelligent model for fault prediction was built. It was trained and verified with the help of the sparrow search algorithm. Its efficacy was demonstrated by testing various deep learning models in the same setting and with identical parameters. The results show that the prediction accuracy of the model is the highest compared with other 11 models, reaching 98.61%. It is suggested that the model using artificial intelligence can provide an accurate fault warning for the oilfield and offer guidance for the maintenance of the rod pumping system, which is meant to reduce the occurrence of production stagnation and resource waste.

Research on Traffic Marking Segmentation Detection Algorithm Based on Feature Fusion

Identifying road markings is a very important part of the vehicle environment sensing system and plays a crucial role in a vehicle’s correct understanding of a current traffic situation. However, road traffic markings are interfered with by a variety of factors, such as being obscured and the viewpoint of the vehicle sensors, resulting in large errors in the existing detection methods. In order to make the target detection task applicable to irregular objects or to detection tasks with higher accuracy requirements while reducing the waste of computational resources, this paper improves the accuracy of traffic marking segmentation detection by designing a multi-type traffic marking segmentation detection model based on image segmentation algorithms and designing a segmentation guidance matrix module based on a rank guidance matrix computation method. By constructing a comprehensive traffic marking detection model, a unified road traffic marking detection is achieved. Finally, the new traffic marking datasets ApolloScape-Precise and ApolloScape-Large are constructed based on the existing ApolloScape dataset, and experimental validation is carried out on these two datasets. The results show that the index MIoU (Mean Intersection over Union) of traffic marking segmentation detection reaches 61.44% and 70.15%, thus achieving a more perfect road traffic marking detection and right-of-way information perception and proving the effectiveness of the integrated traffic marking detection method designed in this paper.

Addressing the Cost Optimization Issue for IOTA Based on Lyapunov Optimization Theory

IOTA is an emerging decentralized computing paradigm for developing blockchain-based Internet of Things (IoT) applications. It has the advantages of zero transaction fees, incremental scalability, and high-performance transaction rates. Despite its well-understood benefits, IOTA nodes need to withstand considerable resource costs to generate the distributed ledger. The main reason for this is that IOTA abandons the original blockchain reward mechanism and does not charge transaction fees. Therefore, in this paper we address the cost optimization issue for IOTA based on Lyapunov optimization theory. We take the first step in investigating the cost optimization problem of IOTA and exploring a new optimization scheme using Lyapunov optimization theory. Our proposed scheme enables IOTA to minimize the total cost of IOTA nodes through a computational optimization algorithm. Then, an optimized transaction rate control algorithm can be designed based on the large deviation theory to reduce orphan tangles that waste computational costs. In addition, we define and deduce the effective width of the tangle to monitor the total throughput and reduce the time spent on cost optimization to avoid unnecessary waste of resources. Lastly, a comprehensive theoretical analysis and simulation experiments demonstrate that the proposed strategy is both efficient and practical.

Vanadium extraction from vanadinite and high carbon v-bearing stone coal by coupling reduction smelting and oxalic acid hydrothermal leaching

Vanadinite and high-carbon vanadium-bearing (V-bearing) stone coals are important vanadium sources widely used in vanadium extraction. The traditional sulfuric acid leaching method for extracting vanadium consumes a large amount of acid, wastes significant amounts of lead and carbon, and causes serious environmental pollution. This study discusses the integration of reduction smelting and oxalic acid hydrothermal leaching for processing vanadinite and high-carbon V-bearing stone coals. Specifically, high-carbon V-bearing stone coal is selectively added to vanadinite to produce coarse lead blocks and V-bearing slag during reduction smelting. Then, the oxalic acid hydrothermal leaching process is employed to extract vanadium from the V-bearing slag. This approach reduces the waste of lead and carbon resources and achieves co-extraction of vanadium from both V-bearing minerals. The results show that when the mass ratio of high-carbon V-bearing stone coal to vanadinite is 25%, and smelting is conducted at 1150°C for 15 min under argon protection, a separation efficiency of 90.67% for metallic lead and V-bearing slag is achieved, the grade of V2O5 in vanadium slag is 12.37%, and the recovery rate is 95.64% by ICP-OES analysis. Furthermore, the leaching rate of vanadium in the V-bearing slag can reach 85.34% via oxalic acid hydrothermal leaching under conditions of a leaching temperature of 100°C, a leaching duration of 30 min, an oxalic acid concentration of 25 wt%, a liquid-to-solid ratio of 10:1 mL/g, and a stirring speed of 500 rpm. The integration of reduction smelting and oxalic acid hydrothermal leaching offers benefits such as improved resource utilization, waste reduction, cost savings, and environmental enhancements. Thus, the findings of this research can serve as references for the vanadium extraction processes of related vanadium-containing minerals.

Techno-economic analysis in the service of traditional ceramic industry. The first review

The traditional ceramics industry uses large amounts of raw materials and energy while significantly contributing to the CO2 footprint. The improvements, primarily in the first two areas, will contribute to sustainable development and answer the emerging questions of decreasing a gas-release footprint. These are topics of high importance considering European Union goals, and thus, on the contrary, there is yet only scarce available literature on the subject. This paper describes the state and future perspectives of the traditional ceramics industry through the techno-economic lens. Many options are detected that save resources (waste materials), energy consumption (reparation or modification of kilns, usage of dry pressing in ceramic tiles production, etc.), and control of flue gas composition (changing the fuel, etc.). Further intensified research is needed to widen the knowledge and compare the results. In the first place, the composition of flue gas should be determined by the same methods and presented using unified units. The lowest energy consumption and flue gas composition are determined in ceramic tiles production by the dry route. Using a downdraft gasifier of biomass in brick manufacturing increases the internal rate of return if a longer tunnel kiln is applied. The expected payback period is from 4 to 8 years.

Integrating AI with Chemical Engineering for Rapid Biomaterial Development in Health Applications

This research presents an innovative approach to healthcare biomaterial development by integrating AI with chemical engineering methodologies. This study combines AI’s predictive power with the precision of chemical engineering to accelerate the discovery and optimization of biomaterials tailored for health applications while focusing on biocompatibility, adaptability, and stringent biomedical functionality. By using machine learning frameworks and advanced simulation software, the project developed predictive models that streamline biomaterial synthesis. This reduced experimental cycles by up to 40% and shortened development timelines by over 30%. Through neural networks and hybrid AI models, material properties such as degradation rate, strength, and biocompatibility can be accurately predicted, which minimizes in vivo testing and accelerates the transition from laboratory research to clinical trials. Protocols optimized through AI-driven insights enhance biomaterial production’s reproducibility and scalability while reducing material costs by 20%, which supports economically viable synthesis methods for large-scale applications. This research contributes a customizable AI framework for health-focused biomaterial solutions and impacts areas such as regenerative medicine, drug delivery, and implantable devices. Future directions include integrating real-time clinical feedback into AI models to improve safety and adaptability, thus advancing sustainable and personalized approaches to biomaterial development. This interdisciplinary framework demonstrates how AI-enhanced chemical engineering workflows can yield adaptive biomaterials suited to specific biomedical needs and offers unprecedented precision in material selection. The model’s high predictive accuracy in biocompatibility screening, achieving over 90%, enables focused experimental validation while reducing the need to rely on animal studies, which fosters ethical research practices. The sustainability impact of this project is significant, as AI-driven optimization reduces resource consumption and waste and aligns with eco-friendly biomaterial development practices. Ultimately, this research paves the way for rapid, efficient, and sustainable innovations in healthcare materials and sets a foundation for future advancements in personalized medical applications.

CO2 methanation: a bibliometric analysis and review of activated carbon-based materials (2014–24)

Abstract This study highlights the significant potential of activated carbon (AC)-based materials in environmental remediation and energy production, particularly in converting carbon dioxide (CO2) and hydrogen (H2) into methane (CH4) and water (H2O) using transition metal-based catalysts. It emphasizes the role of porous AC in waste reduction and resource utilization, examining various applications of CO2 and evaluating environmental impacts. The research explores commercialization opportunities and specifically investigates CO2 methanation using AC-based materials. Using bibliometric analyses of 4196 articles from the Web of Science database, the study identifies a growing research interest in porous AC-related CO2 methanation from 2014 to 2024. The top three journals in this field are Environment Development and Sustainability, Biomass Conversion and Biorefinery, and Journal of Environment Science and Pollution. However, there is limited inter-institutional collaboration in this field, suggesting room for development towards commercializing sustainable CH4 production pathways. CH4 is highlighted as a crucial intermediate in industrial processes, and research directions are identified through co-occurring author keywords analysis. The study suggests the need for a comprehensive approach integrating AC materials into carbon-neutral energy processes while addressing the potential adverse effects of AC nanoparticles on biological and environmental factors. Ultimately, it clarifies the potential uses and commercialization prospects for porous AC materials, especially in conjunction with carbon capture and utilization technologies, promoting sustainable practices in energy production and environmental management.

Optimization of Amaranthus production under irrigation and poultry manure application using grey relational analysis

This study was carried out to optimize Amaranthus production under varying irrigation levels and poultry manure applications using Grey Relational Analysis. The experimental design was split-plot, where irrigation treatment was the main plot, while poultry manure was the subplot treatment. The irrigation levels are FIT50, FIT75, FIT100 and FIT135 which correspond to 50% Full irrigation, 75% full irrigation, 100% full irrigation and 135% full irrigation treatments, in combination with the poultry manure application rates M0t/ha, M5t/ha, M10t/ha and M20t/ha. Sixteen experimental treatments were arranged in a 4 x 4 factorial experimental design and replicated 3 times. Sprinkler irrigation system was adopted for the study. Results showed that there was corresponding increase in the response of Amaranthus growth characteristics and yield to the combination of irrigation and poultry manure treatments. The Crop Water-use efficiency initially showed a linear corresponding increase to an average value of 473kg/ha/mm but later exhibits a diminishing return beyond FIT100 irrigation. While production cost showed continual increase to average value of $377/ha as the inputs treatment level also increased to FIT135 irrigation combined with the poultry manure M20t/ha. Grey Relational Grades (GRG) calculated were 0.854, 0.790, 0.787, 0.765, 0.740, 0.629, 0.621, 0.620, 0.546, 0.543, 0.515, 0.475, 0.458, 0.443, 0.417 and 0.403 respectively. Amaranthus cultivated under FIT100_M20t/ha treatment matched with the highest GRG value 0.854, making FIT100_M20t/ha treatment the optimum. Analysis of variance for the GRG showed that irrigation and poultry manure are highly significant input variables for Amaranthus production. The findings from this study guide farmers on the optimal combination of water and organic fertilizer to maximize Amaranthus yield and minimize resource waste, thereby enhancing sustainability in crop production.

Energizing the future: Unleashing the potential of innovative waste-to-energy technologies for energy development and sustainability within Zimbabwe's tourism sector.

Zimbabwe's tourism industry, renowned for its natural wonders and cultural heritage, faces a looming energy crisis rooted in the detrimental over-reliance on fossil fuels and the underutilization of substantial waste resources that lie dormant. The article investigates multifaceted relationship between six independent variables: landfill gas recovery and anaerobic digestion, pyrolysis and gasification, incineration, biogas production, biodiesel production, ethanol production and syngas fermentation and one dependent variable: energy development and sustainability. In this study, a quantitative methodology was adopted, involving the gathering of data from 519 stakeholders in the tourism supply chain through a simple random sampling technique, with the sample size determined using the Krejcie and Morgan table. The distribution of questionnaires was facilitated through Google Forms, and the data analysis was conducted using Smart PLS. Statistical findings indicate direct significant relationship between variables, and t-statistic values all hypotheses were all greater than the threshold of 1.96, ranging from a minimum of 2.911 to a maximum of 9.431. These findings underscore the robustness of the relationships between the waste-to-energy technologies and energy development and sustainability within Zimbabwe's tourism sector. This empirical evidence highlights the substantial potential for these innovative technologies to play a pivotal role in mitigating the energy crisis and fostering sustainable energy development.

MemoriaNova: Optimizing Memory-Aware Model Inference for Edge Computing

In recent years, deploying deep learning models on edge devices has become pervasive, driven by the increasing demand for intelligent edge computing solutions across various industries. From industrial automation to intelligent surveillance and healthcare, edge devices are being leveraged for real-time analytics and decision-making. Existing methods face two challenges when deploying machine learning models on edge devices. The first challenge is handling the execution order of operators with a simple strategy, which can lead to a potential waste of memory resources when dealing with directed acyclic graph structure models. The second challenge is that they usually process operators of a model one by one to optimize the inference latency, which may lead to the optimization problem getting trapped in local optima. We present MemoriaNova, comprising BTSearch and GenEFlow, to solve these two problems. BTSearch is a graph state backtracking algorithm with efficient pruning and hashing strategies designed to minimize memory overhead during inference and enlarge latency optimization search space. GenEFlow, based on genetic algorithms, integrates latency modeling and memory constraints to optimize distributed inference latency. This innovative approach considers a comprehensive search space for model partitioning, ensuring robust and adaptable solutions. We implement BTSearch and GenEFlow and test them on eleven deep-learning models with different structures and scales. The results show that BTSearch can reach 12% memory optimization compared with the widely used random execution strategy. At the same time, GenEFlow reduces inference latency by 33.9% in distributed systems with four-edge devices.