Hasil untuk "Municipal refuse. Solid wastes"

Yumuna Chenjerai Tombe, Gladman Thondhlana, Sheunesu Ruwanza

Illegal solid waste dumping is a key urban sustainability challenge due to increased urbanisation and human consumption, but its prevalence and impacts across a socially differentiated gradient are seldom considered. We used street and off-street road surveys to examine the extent of illegal solid waste dumping across an income gradient in two medium-sized towns of Makhanda and Knysna in South Africa. We enumerated all dumpsites encountered in low- and high-income areas, recorded their GPS coordinates, and visually estimated size and composition using a standardised typology. We encountered 215 illegal solid waste dumpsites unevenly distributed by town (155 in Makhanda and 60 in Knysna) and income status, with the majority located in low-income areas compared to high-income areas. Most illegal solid waste dumpsites in low-income areas were small and located along roadsides and vacant plots. In both towns, illegal solid waste dumpsites were dominated by household and garden waste. The findings suggest that social differentiation matters in illegal solid waste dumping and should be factored into service provision strategies for ensuring environmental justice. We recommend that (i) municipalities should consider income heterogeneity in designing effective and equitable waste management plans, (ii) the national government should consider additional human and financial support to municipalities for efficient and equitable residential waste management, (iii) waste recycling at source (within households) should be mainstreamed in waste management strategies, and (iv) cleanup campaigns should be considered as a short-term solution to manage existing illegal solid waste dumpsites.

Tanay Gondil

Large language models are trained to refuse harmful requests, but can they accurately predict when they will refuse before responding? We investigate this question through a systematic study where models first predict their refusal behavior, then respond in a fresh context. Across 3754 datapoints spanning 300 requests, we evaluate four frontier models: Claude Sonnet 4, Claude Sonnet 4.5, GPT-5.2, and Llama 3.1 405B. Using signal detection theory (SDT), we find that all models exhibit high introspective sensitivity (d' = 2.4-3.5), but sensitivity drops substantially at safety boundaries. We observe generational improvement within Claude (Sonnet 4.5: 95.7 percent accuracy vs Sonnet 4: 93.0 percent), while GPT-5.2 shows lower accuracy (88.9 percent) with more variable behavior. Llama 405B achieves high sensitivity but exhibits strong refusal bias and poor calibration, resulting in lower overall accuracy (80.0 percent). Topic-wise analysis reveals weapons-related queries are consistently hardest for introspection. Critically, confidence scores provide actionable signal: restricting to high-confidence predictions yields 98.3 percent accuracy for well-calibrated models, enabling practical confidence-based routing for safety-critical deployments.

Tony Cristofano

Safety-aligned language models systematically refuse harmful requests. While activation steering can modulate refusal, ablating the raw "refusal vector" calculated from contrastive harmful and harmless prompts often causes collateral damage and distribution drift. We argue this degradation occurs because the raw vector is polysemantic, entangling the refusal signal with core capability circuits and linguistic style. We introduce Surgical Refusal Ablation (SRA) to distill these steering directions. SRA constructs a registry of independent Concept Atoms representing protected capabilities and stylistic confounds, then uses ridge-regularized spectral residualization to orthogonalize the refusal vector against these directions. This yields a clean refusal direction that targets refusal-relevant structure while minimizing disruption to the model's semantic geometry. Across five models (Qwen3-VL and Ministral series), SRA achieves deep refusal reduction (0-2%) with negligible perplexity impact on Wikitext-2 (mean delta PPL approx. 0.02) and minimal distribution drift. Notably, standard ablation on Qwen3-VL-4B induces severe drift (first-token KL = 2.088), whereas SRA maintains the original distribution (KL = 0.044) while achieving the same 0% refusal rate. Using teacher-forced perplexity on GSM8K and MBPP as a high-resolution capability proxy, we show SRA preserves math and code distributions. These results suggest that common "model damage" is often "Ghost Noise," defined as the spectral bleeding of the dirty refusal direction into capability subspaces.

Hyundong Jin, Dongyoon Han, Eunwoo Kim

Continual unlearning poses the challenge of enabling large vision-language models to selectively refuse specific image-instruction pairs in response to sequential deletion requests, while preserving general utility. However, sequential unlearning updates distort shared representations, creating spurious associations between vision-language pairs and refusal behaviors that hinder precise identification of refusal targets, resulting in inappropriate refusals. To address this challenge, we propose a novel continual unlearning framework that grounds refusal behavior in fine-grained descriptions of visual and textual concepts decomposed from deletion targets. We first identify which visual-linguistic concept combinations characterize each forget category through a concept modulator, then determine how to generate appropriate refusal responses via a mixture of refusal experts, termed refusers, each specialized for concept-aligned refusal generation. To generate concept-specific refusal responses across sequential tasks, we introduce a multimodal, concept-driven routing scheme that reuses refusers for tasks sharing similar concepts and adapts underutilized ones for novel concepts. Extensive experiments on vision-language benchmarks demonstrate that the proposed framework outperforms existing methods by generating concept-grounded refusal responses and preserving the general utility across unlearning sequences.

Thomas Heverin

Prompt injection evaluations typically treat refusal as a stable, binary indicator of safety. This study challenges that paradigm by modeling refusal as a local decision boundary and examining its stability under structured perturbations. We evaluated two models, GPT-4.1 and GPT-4o, using 3,274 perturbation runs derived from refusal-inducing prompt injection attempts. Each base prompt was subjected to 25 perturbations across five structured families, with outcomes manually coded as Refusal, Partial Compliance, or Full Compliance. Using chi-square tests, logistic regression, mixed-effects modeling, and a novel Refusal Boundary Entropy (RBE) metric, we demonstrate that while both models refuse >94% of attempts, refusal instability is persistent and non-uniform. Approximately one-third of initial refusal-inducing prompts exhibited at least one "refusal escape," a transition to compliance under perturbation. We find that artifact type is a stronger predictor of refusal failure than perturbation style. Textual artifacts, such as ransomware notes, exhibited significantly higher instability, with flip rates exceeding 20%. Conversely, executable malware artifacts showed zero refusal escapes in both models. While GPT-4o demonstrated tighter refusal enforcement and lower RBE than GPT-4.1, it did not eliminate artifact-dependent risks. These findings suggest that single-prompt evaluations systematically overestimate safety robustness. We conclude that refusal behavior is a probabilistic, artifact-dependent boundary phenomenon rather than a stable binary property, requiring a shift in how LLM safety is measured and audited.

Xinrui Li, Takehiko Murayama, Shigeo Nishikizawa et al.

In recent years, China has made strong national commitments to waste reduction and circular economy, including the implementation of mandatory municipal solid waste separation policies and the rollout of zero-waste city initiatives. These efforts represent a strategic shift toward systemic environmental governance. However, the outbreak of the COVID-19 pandemic in early 2020—and the subsequent implementation of the country’s stringent zero-COVID policy—led to an abrupt disruption of these programs. Under this policy, strict lockdowns, quarantine of both confirmed and suspected cases, and city-wide containment became top priorities, sidelining environmental initiatives such as waste separation and sustainable waste infrastructure development. This study investigates how Chinese residents’ motivations for waste separation evolved across three key phases: pre-pandemic, during the zero-COVID enforcement period, and post-pandemic recovery. Grounded in the Theory of Planned Behavior and pro-environmental behavior theory, we developed an extended model incorporating pandemic-related social, psychological, and policy variables. Based on 526 valid questionnaire responses collected in late 2023 in Shanghai, we conducted structural equation modeling and repeated-measures analysis. Findings reveal a significant shift from externally driven compliance—reliant on governmental enforcement and service provision—to internally motivated behavior based on environmental values and personal efficacy. This transition was most evident after the pandemic, suggesting the potential for sustained pro-environmental habits despite weakened policy enforcement. Our findings underscore the importance of strengthening internal drivers in environmental governance, especially under conditions where policy continuity is vulnerable to systemic shocks such as public health emergencies.

Sotiris Kottaridis, Christina Drosou, Christos Boukouvalas et al.

In this study, a Life Cycle Assessment (LCA) was conducted to evaluate the environmental impact of osmotically dehydrated, fresh-cut, pre-packaged potatoes compared to conventional untreated ones. The case study focused on a small processing line in Naxos Island, Greece, aiming to extend shelf-life by up to 5 days. The analysis covered the full value chain, from cultivation to household consumption, considering changes in energy and material use, transport volumes, waste generation, and cultivation demand. Three scenarios were assessed: (i) conventional untreated potatoes, (ii) dehydrated potatoes using market glycerol, and (iii) dehydrated potatoes using glycerol from vegetable oil treatment. Systems and life cycle inventories (LCI) were modelled in OpenLCA v2.4 software with the ecoinvent v3.11 database, applying the Environmental Footprint (EF) method, v3.1. The selected impact categories included the following: global warming potential, water use, freshwater ecotoxicity, freshwater and marine eutrophication, energy resource use, particulate matter formation, and acidification. Results showed that applying osmotic dehydration (OD) improved environmental performance in most, but not all, categories. When market glycerol was used, some burdens increased due to glycerol production. However, using glycerol from vegetable oil treatment resulted in reductions of 25.8% to 54.9% across all categories compared to the conventional scenario. Overall, OD with alternative glycerol proved to be the most environmentally beneficial approach.

Riad Ahmed Anonto, Md Labid Al Nahiyan, Md Tanvir Hassan

Safety-aligned language models often refuse prompts that are actually harmless. Current evaluations mostly report global rates such as false rejection or compliance. These scores treat each prompt alone and miss local inconsistency, where a model accepts one phrasing of an intent but rejects a close paraphrase. This gap limits diagnosis and tuning. We introduce "semantic confusion," a failure mode that captures such local inconsistency, and a framework to measure it. We build ParaGuard, a 10k-prompt corpus of controlled paraphrase clusters that hold intent fixed while varying surface form. We then propose three model-agnostic metrics at the token level: Confusion Index, Confusion Rate, and Confusion Depth. These metrics compare each refusal to its nearest accepted neighbors and use token embeddings, next-token probabilities, and perplexity signals. Experiments across diverse model families and deployment guards show that global false-rejection rate hides critical structure. Our metrics reveal globally unstable boundaries in some settings, localized pockets of inconsistency in others, and cases where stricter refusal does not increase inconsistency. We also show how confusion-aware auditing separates how often a system refuses from how sensibly it refuses. This gives developers a practical signal to reduce false refusals while preserving safety.

John Halloran

The model context protocol (MCP) has been widely adapted as an open standard enabling the seamless integration of generative AI agents. However, recent work has shown the MCP is susceptible to retrieval-based "falsely benign" attacks (FBAs), allowing malicious system access and credential theft, but requiring that users download compromised files directly to their systems. Herein, we show that the threat model of MCP-based attacks is significantly broader than previously thought, i.e., attackers need only post malicious content online to deceive MCP agents into carrying out their attacks on unsuspecting victims' systems. To improve alignment guardrails against such attacks, we introduce a new MCP dataset of FBAs and (truly) benign samples to explore the effectiveness of direct preference optimization (DPO) for the refusal training of large language models (LLMs). While DPO improves model guardrails against such attacks, we show that the efficacy of refusal learning varies drastically depending on the model's original post-training alignment scheme--e.g., GRPO-based LLMs learn to refuse extremely poorly. Thus, to further improve FBA refusals, we introduce Retrieval Augmented Generation for Preference alignment (RAG-Pref), a novel preference alignment strategy based on RAG. We show that RAG-Pref significantly improves the ability of LLMs to refuse FBAs, particularly when combined with DPO alignment, thus drastically improving guardrails against MCP-based attacks.

Aryan Shrivastava, Ari Holtzman

Most commonly used language models (LMs) are instruction-tuned and aligned using a combination of fine-tuning and reinforcement learning, causing them to refuse users requests deemed harmful by the model. However, jailbreak prompts can often bypass these refusal mechanisms and elicit harmful responses. In this work, we study the extent to which information accessed via jailbreak prompts is decodable using linear probes trained on LM hidden states. We show that a great deal of initially refused information is linearly decodable. For example, across models, the response of a jailbroken LM for the average IQ of a country can be predicted by a linear probe with Pearson correlations exceeding $0.8$. Surprisingly, we find that probes trained on base models (which do not refuse) sometimes transfer to their instruction-tuned versions and are capable of revealing information that jailbreaks decode generatively, suggesting that the internal representations of many refused properties persist from base LMs through instruction-tuning. Importantly, we show that this information is not merely "leftover" in instruction-tuned models, but is actively used by them: we find that probe-predicted values correlate with LM generated pairwise comparisons, indicating that the information decoded by our probes align with suppressed generative behavior that may be expressed more subtly in other downstream tasks. Overall, our results suggest that instruction-tuning does not wholly eliminate or even relocate harmful information in representation space-they merely suppress its direct expression, leaving it both linearly accessible and indirectly influential in downstream behavior.

Zulqarnain Ahmad Ali, Joshua M. Werner

An investigation has been carried out to understand the solution chemistry of the Cu-NH⁻-SO₄⁻² system, focusing on the effect of pH on the solubility of copper in the solution and maximizing the Cu(I):Cu(II) ratio. A Pourbaix diagram for the Cu-N-S system has also been created using the HSC Chemistry software for a wide range of Cu-NH₃ species, unlike most other studies that focused only on Cu(NH₃)₄²⁺ and Cu(NH₃)₅²⁺ (Cu(II)) as the dominant species. The Pourbaix diagram demonstrated that the Cu(I) exists as Cu(NH₃)₂⁺, while the Cu(II) species are present in the system as Cu(NH₃)₄²⁺ and Cu(NH₃)₅²⁺, depending upon the Eh and pH of the solution. Copper precipitation was observed in the electrolyte at pH values less than 8.0, and the precipitation behavior increased as the pH became acidic. The highest Cu(I):Cu(II) ratio was observed at higher pH values of 10.05 due to the higher solubility of copper at higher alkaline pH. The maximum Cu(II) concentration can be achieved at 4.0 M NH₄OH and 0.76 M (NH₄)₂SO₄. In the case of low pH, the highest Cu(I):Cu(II) ratio obtained was 0.91 against the 4.0 M and 0.25 M concentrations of NH₄OH and (NH₄)₂SO₄, respectively. Meanwhile, at high pH, the maximum Cu(I):Cu(II) ratio was 15.11 against the 0.25 M (NH₄)₂SO₄ and 4.0 M NH₄OH. Furthermore, the low pH experiments showed the equilibrium constant (K) K < 1, and the high pH experiments demonstrated K > 1, which justified the lower and higher copper concentrations in the solution, respectively.

Nontobeko Gloria Maphuhla, Opeoluwa Oyehan Oyedeji

Inadequate waste management and illegal trash dumping continue to be the leading causes of severe environmental pollution. Human exposure to harmful heavy metals has emerged as a serious health concern on the continent. Some people in Alice, a small town, grow their food in home gardens. They use animal manure and compost derived from soil obtained from landfills to enhance the fertility of the garden soil. Heavy metal heaps in garbage disposals are constantly present, releasing dangerous amounts of metal into the environment. The harmful effects of heavy metals on plants lead to unsanitary conditions and environmental problems. Animals and people who consume these vegetables may also be at risk for health problems. Assessing the soil’s enzyme activity can potentially lessen the negative effects of the accumulated pollutants and improve the soil’s overall health and quality. Soil enzymes are biologically active components that have a catalytic impact and are released from root exudates, crop residues, and animal remains. The activity of enzymes serves as an excellent bioindicator of soil cleanliness and quality because they are sensitive to heavy metals. X-ray diffraction (XRD) was used to quantify the mineral elements in soil using 40 kV parallel beam optics, 30 mA, and CuKα radiation. Meanwhile, the activity of the enzyme was essayed in different coupled substrates. Thirteen (13) clay minerals were found, including Talc 2M, Kaolinite 2M, and Chlorite Lawsonite Muscovite 2M1. The detected trace elements have high concentration levels that exceed the World Health Organization’s (WHO) allowed levels. The identified elements affected the enzyme activity at different levels. The Mn, Al, Si, V, Ti, and Ca negatively affect soil enzyme activity, specifically invertase (INV). However, the amount of Mg, K, Fe, and Zn showed a slightly positive effect on the same enzyme (INV). According to this view, these elements come from several sources, each with a particular impact on soil contamination and enzyme activity. High levels of heavy metals in this study may be due to improper waste disposal, limited recycling opportunities, lack of public awareness, and inadequate enforcement of waste management regulations. It is essential to employ Fourth Industrial Revolution (4IR) technologies, correct disposal techniques, suitable agricultural methods, preventive regulations, and efficient waste management to mitigate the negative effects of heavy metals on the environment.

Khin Zaw Win, Helmut Yabar, Takeshi Mizunoya

Data on waste generation and composition are fundamental for effective waste management and can vary over time. Assessing the allocation of waste management facilities is also important to improve the entire waste management system, including land management. A survey conducted among 108 households in both urban and rural areas across six townships analyzed the waste generation and physical composition in Mandalay, highlighting the current trends relating to waste. Concurrently, data on current waste management facilities were gathered. The average waste generation is 0.84 kg/person/day, with urban areas producing 0.91 kg/person/day and rural areas 0.37 kg/person/day. The per capita waste generation rate reported in this study exceeds those in most previous studies conducted in Mandalay up to 2020, as well as the national average and that of most cities in Myanmar. Organic waste constitutes most of the physical composition, accounting for 82.3%, followed by plastic waste (10.7%), paper and cardboard (3.2%), glass (0.9%), metal (0.8%), leather and fabric (0.4%), and other waste (1.7%). Rural areas produce a higher percentage of most types of waste compared with urban areas, except for organic waste. Surprisingly, urban areas produce waste with a higher organic composition compared with rural areas. The percentage of organic waste was found to be higher than in previous studies conducted in Mandalay and other cities. Proper management of organic waste could significantly reduce the burden on waste management. In order to achieve this goal, this study proposes several viable strategies for optimizing solid waste management in Mandalay. The current location of waste management facilities reflects the efficiency of waste management and accessibility. However, there are concerns about this and improvements are necessary. These can be achieved by optimizing the placement of waste management facilities and enhancing the efficiency of the collection and transportation sector.

Yuhao Wang, Zhiyuan Zhu, Heyang Liu et al.

Multimodal large language models (MLLMs) excel at multimodal perception and understanding, yet their tendency to generate hallucinated or inaccurate responses undermines their trustworthiness. Existing methods have largely overlooked the importance of refusal responses as a means of enhancing MLLMs reliability. To bridge this gap, we present the Information Boundary-aware Learning Framework (InBoL), a novel approach that empowers MLLMs to refuse to answer user queries when encountering insufficient information. To the best of our knowledge, InBoL is the first framework that systematically defines the conditions under which refusal is appropriate for MLLMs using the concept of information boundaries proposed in our paper. This framework introduces a comprehensive data generation pipeline and tailored training strategies to improve the model's ability to deliver appropriate refusal responses. To evaluate the trustworthiness of MLLMs, we further propose a user-centric alignment goal along with corresponding metrics. Experimental results demonstrate a significant improvement in refusal accuracy without noticeably compromising the model's helpfulness, establishing InBoL as a pivotal advancement in building more trustworthy MLLMs.

Niels A. Wouda, Marjolein Aerts-Veenstra, Nicky van Foreest

We study a daily urban waste collection problem arising in the municipality of Groningen, The Netherlands, where residents bring their waste to local underground waste containers organised in clusters. The municipality plans routes for waste collection vehicles to empty the container clusters. These routes should be as short as possible to limit operational costs, but also long enough to visit sufficiently many clusters and ensure that containers do not overflow. A complicating factor is that the actual fill levels of the clusters' containers are not known, and only the number of deposits is observed. Additionally, it is unclear whether the containers should be upgraded with expensive fill level sensors so that the service level can be improved or routing costs can be reduced. We propose an efficient integrated selection and routing (ISR) policy that jointly optimises the daily cluster selection and routing decisions. The integration is achieved by first estimating prizes that express the urgency of selecting a cluster to empty, and then solving a prize-collecting vehicle routing problem with time windows and driver breaks to collect these prizes while minimising routing costs. We use a metaheuristic to solve the prize-collecting vehicle routing problem inside a realistic simulation environment that models the waste collection problem faced by the municipality. We show that solving the daily waste collection problem in this way is very effective, and can lead to substantial cost savings for the municipality in practice, with no reduction in service level. In particular, by integrating the container selection and routing problems using our ISR policy, routing costs can be reduced by more than 40% and the fleet size by 25%. We also show that more advanced measuring techniques do not significantly reduce routing costs, and the service level not at all.

Runchuan Zhu, Zhipeng Ma, Jiang Wu et al.

Refusal-Aware Instruction Tuning (RAIT) enables Large Language Models (LLMs) to refuse to answer unknown questions. By modifying responses of unknown questions in the training data to refusal responses such as "I don't know", RAIT enhances the reliability of LLMs and reduces their hallucination. Generally, RAIT modifies training samples based on the correctness of the initial LLM's response. However, this crude approach can cause LLMs to excessively refuse answering questions they could have correctly answered, the problem we call over-refusal. In this paper, we explore two primary causes of over-refusal: Static conflict occurs when similar samples within the LLM's feature space receive differing supervision signals (original vs. modified "I don't know"). Dynamic conflict arises as the LLM's evolving knowledge during SFT enables it to answer previously unanswerable questions, but the now-answerable training samples still retain the original "I don't know" supervision signals from the initial LLM state, leading to inconsistencies. These conflicts cause the trained LLM to misclassify known questions as unknown, resulting in over-refusal. To address this issue, we introduce Certainty Represented Knowledge Flow for Refusal-Aware Instructions Tuning (CRaFT). CRaFT centers on two main contributions: First, we additionally incorporate response certainty to selectively filter and modify data, reducing static conflicts. Second, we implement preliminary rehearsal training to characterize changes in the LLM's knowledge state, which helps mitigate dynamic conflicts during the fine-tuning process. We conducted extensive experiments on open-ended question answering and multiple-choice question task. Experiment results show that CRaFT can improve LLM's overall performance during the RAIT process. Code and data will be released at https://github.com/opendatalab/CRaFT .

Diego Rossit, Adrián Toncovich

The compactness of routes in distribution plans is a criterion that has not been sufficiently explored in the literature related to logistics distribution but has shown to have a significant impact on the practical implementation of routing plans, for example in solid waste collection problems. In this regard, this article presents a bi-objective model to optimize the vehicle routing problem with time constraints, considering the minimization of travel times and the compactness of routes. Experimental tests were conducted on small-scale instances using two exact solution methods for multi-objective problems: weighted sum and augmented ε-constraint methods. The results obtained allowed us to explore the trade-off relationship between both objectives while evaluating the computational efficiency of both multi-objective solution methods.

Saliha Keita, Srecko Stopic, Ferdinand Kiessling et al.

Cobalt’s pivotal role in global development, especially in lithium-ion batteries, entails driving increased demand and strengthening global trading networks. The production of different waste solutions in metallurgical operations requires the development of an environmentally friendly research strategy. The ultrasonic spray pyrolysis and hydrogen reduction method were chosen to produce nanosized magnetic powders from waste solution based on iron and cobalt obtained during the purification process of used polycrystalline diamond blanks. With specific objectives focused on investigating the impact of reaction temperature and residence time on the morphology, chemical composition, and crystal structure of synthesized nanosized cobalt powders, our research involved 15 experimental runs using two reactors with varying residence times (7.19 s and 23 s) and distinct precursors (A, B, and C). Aerosol droplets were reduced at 600 to 900 °C with a flow rate of 3 L/min of argon and hydrogen (1:2). Characterization via scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction revealed that higher temperatures influenced the spherical particle morphology. Altering cobalt concentration in the solution impacted the particle size, with higher concentrations yielding larger particles. A short residence time (7.9 s) at 900 °C proved optimal for cobalt submicron synthesis, producing spherical particles ranging from 191.1 nm to 1222 nm. This research addresses the environmental significance of recovering magnetic particles from waste solutions, contributing to sustainable nanomaterial applications.

Claudio Marciano

Waste management is one of the most strategic areas of regional policy planning. The impact of decisions such as the allocation of industrial waste treatment plants and waste collection strategies can affect the economic structure and quality of life of territories. The effectiveness of regulatory and organisational arrangements of Regional Waste Plans is linked to the availability of technologies and material infrastructure, but also to social consensus and behaviours. On this level, participatory planning conducted through foresight techniques plays an increasing role. The article presents an innovative case carried out in Valle d’Aosta in 2021, with the aim of promoting the participatory methodology experimented and the institutionalisation of such applications in strategic waste planning processes. The process involved 35 different stakeholders (unions, businesses, schools, trade, environmental associations, etc.) in structured consultations based on the principle of building a shared transition to 2030. In particular, the project was effective in broadening the participation of civil society in the area, in making the plan’s objectives more ambitious, and in fostering the creation of a collaborative network between public, market and third sector actors.

Adam Lyndsell, James M. Buick

In practical applications, waste water piping includes elbows and bends which give unrepeatable, asymmetric and swirling flow profiles, which result in flow meter inaccuracy. Flow conditioners can be inserted into the pipe network to remove these flow patterns prior to a flow meter, to improve the accuracy of the measurement and to reduce the length of straight-run which would otherwise be required. In this investigation, a new design of flow conditioner is considered in two configurations, with and without vanes. The performance of the conditioner is considered by exposing it to a swirling flow that was disturbed by two 90° bends. The flow downstream of the conditioner was simulated using CFD software STAR-CCM+ 12 to find the downstream axial velocity profile, swirl angle and pressure drop. The vane-less conditioner provided a suitable axial profile for flow measurement 2D downstream, at which point the swirl was removed. This illustrated the improved performance compared to other conditioners in the literature, but came at the price of a somewhat higher pressure drop. The addition of vanes improved the performance slightly in terms of regulating the flow and removing swirl, while at the same time increasing the pressure drop further.