Waste management and recycling operations generate extensive documentation: collection route logs, contamination reports, regulatory compliance filings, landfill monitoring records, hazardous waste manifests, and sustainability disclosures. Chinese LLMs like DeepSeek V4, GLM-4, and Qwen3 can process this complex operational data to optimize routes, ensure compliance, and accelerate reporting. TokenEase's unified API provides waste management companies with cost-effective access to these powerful models.
Transform service requests, traffic data, and vehicle constraints into optimized collection routes with natural language reasoning.
import requests
def optimize_collection_route(stops, vehicle_constraints, traffic_conditions, priority_requests):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": "Optimize waste collection routes considering traffic, vehicle capacity, time windows, and priority stops. Minimize distance and fuel consumption while meeting service commitments."},
{"role": "user", "content": f"Traffic: {traffic_conditions}\nVehicle: {vehicle_constraints}\nPriority: {priority_requests}\nStops:\n{stops}\n\nOptimize route."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
stops = """
1. Downtown Plaza (commercial) - 2 dumpsters, every Mon/Wed/Fri
2. Oakwood Apartments (residential) - 8 bins, daily
3. Riverside Industrial Park - 4 roll-offs, Mon/Thu
4. City Hospital (medical waste) - 3 containers, daily before 8 AM
5. Westside School - 2 bins, Tue/Thu
"""
vehicle = "Rear-loader, 20-yard capacity, current load 40%, fuel 75%, driver shift ends 16:00"
traffic = "Riverside area: road construction 09:00-15:00. Downtown: heavy traffic 07:30-09:00."
priority = "Hospital pickup must complete by 08:00. Industrial Park closes at 15:00."
route = optimize_collection_route(stops, vehicle, traffic, priority)
Analyze contamination reports and photographic descriptions to classify waste streams and recommend education campaigns.
def analyze_contamination(contamination_reports, material_stream, facility_capabilities):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": "Analyze recycling contamination data. Identify top contaminants by source, assess impact on processing, and recommend targeted education or operational changes."},
{"role": "user", "content": f"Facility: {facility_capabilities}\nStream: {material_stream}\nReports:\n{contamination_reports}\n\nAnalyze and recommend."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
reports = """
Week 32: 23% contamination rate (target <15%). Top contaminants: plastic bags (8%), food residue (6%), Styrofoam (4%), electronics (3%), textiles (2%)
Source analysis: Oakwood Apartments highest at 31%. Downtown Plaza lowest at 12%.
"""
stream = "Single-stream curbside recycling: paper, cardboard, plastics #1-7, glass, metal"
capabilities = "MRF processes 150 tons/day. Plastic bags jam sorting equipment. Food residue contaminates paper bales."
analysis = analyze_contamination(reports, stream, capabilities)
Classify waste materials and generate compliant hazardous waste manifests and shipping papers.
def classify_hazardous_waste(waste_description, generator_info, disposal_facility_requirements):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "glm-4-plus",
"messages": [
{"role": "system", "content": "Classify hazardous waste according to EPA/DOT regulations. Determine proper shipping names, hazard classes, and generate compliant manifest documentation."},
{"role": "user", "content": f"Facility: {disposal_facility_requirements}\nGenerator: {generator_info}\nWaste:\n{waste_description}\n\nClassify and generate manifest."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
waste = """
Source: Metal finishing shop
Description: Spent acid bath solution, pH 1.2, contains chromium (45 mg/L), nickel (28 mg/L), copper (12 mg/L)
Volume: 200 gallons
Container: DOT-approved drums, UN 1A2
"""
generator = "Small quantity generator, EPA ID: CA123456789, monthly generation: 150-300 lbs"
facility = "TSDF accepts D002 (corrosive), D007 (chromium), D006 (cadmium - verify nickel acceptance)"
manifest = classify_hazardous_waste(waste, generator, facility)
Analyze landfill monitoring data and generate compliance reports for groundwater, gas, and leachate management.
def generate_landfill_report(monitoring_data, permit_conditions, inspection_findings):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "kimi-k2",
"messages": [
{"role": "system", "content": "Generate landfill compliance reports. Compare monitoring data against permit limits, identify violations or trends, and recommend corrective actions."},
{"role": "user", "content": f"Permit: {permit_conditions}\nInspections: {inspection_findings}\nData:\n{monitoring_data}\n\nGenerate compliance report."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
data = """
Groundwater Q2: MW-01: VOCs ND, metals below detection. MW-02: TCE 2.8 ug/L (limit 5.0). MW-03: TCE 4.1 ug/L (limit 5.0), trending up from 1.2 ug/L in Q1.
Leachate: Flow 45,000 gpd. BOD 2,800 mg/L. pH 6.2. Heavy metals all below permit limits.
Gas: Methane 52% (target >50% for energy recovery). Collection efficiency 78% (target >75%).
"""
permit = "RCRA Subtitle D. Groundwater monitoring: 4 wells quarterly. Leachate: continuous monitoring. Gas: monthly reports."
inspections = "EPA inspection Feb 2026: minor findings on cover maintenance. All corrected."
report = generate_landfill_report(data, permit, inspections)
Generate sustainability reports by analyzing operational data, calculating diversion rates, and documenting environmental impact.
def generate_sustainability_report(operational_data, baseline_year, reporting_framework):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "deepseek-v4",
"messages": [
{"role": "system", "content": f"Generate waste management sustainability reports following {reporting_framework}. Calculate metrics, document progress, and provide narrative context."},
{"role": "user", "content": f"Framework: {reporting_framework}\nBaseline: {baseline_year}\nOperations:\n{operational_data}\n\nGenerate report."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
ops = """
2026 total waste processed: 125,000 tons
Landfilled: 68,000 tons (54.4%)
Recycled: 42,000 tons (33.6%)
Composted: 12,000 tons (9.6%)
Waste-to-energy: 3,000 tons (2.4%)
Fleet: 45 vehicles, 60% diesel, transitioning to CNG
Emissions: 8,500 MT CO2e (down from 9,200 in 2025)
"""
base = "2023: 110,000 tons, 62% landfilled, 28% recycled, 8% composted, 2% WTE"
framework = "GRI 306 (Waste), CDP, SASB"
sustainability = generate_sustainability_report(ops, base, framework)
Create clear, actionable recycling guidance for residents and businesses to reduce contamination and improve diversion rates.
def generate_education_content(target_audience, common_mistakes, program_rules, communication_channel):
response = requests.post(
"https://tokenease.io/v1/chat/completions",
headers={"Authorization": "Bearer YOUR_TOKENEASE_API_KEY"},
json={
"model": "qwen3-235b",
"messages": [
{"role": "system", "content": f"Write clear waste management and recycling educational content for {target_audience}. Use accessible language, address common mistakes, and motivate behavior change."},
{"role": "user", "content": f"Channel: {communication_channel}\nRules: {program_rules}\nMistakes: {common_mistakes}\nAudience: {target_audience}\n\nGenerate content."}
]
}
)
return response.json()["choices"][0]["message"]["content"]
mistakes = "Putting plastic bags in recycling, not rinsing containers, including Styrofoam, wish-cycling broken glass and electronics"
rules = "Single stream: paper, cardboard, clean containers #1-7, metal cans, glass bottles. No bags, no food residue, no Styrofoam."
audience = "Multilingual apartment residents with varying recycling familiarity"
channel = "Social media post series + door hanger"
education = generate_education_content(audience, mistakes, rules, channel)
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