MACKORN Hydraulic Cone Crusher — Selection, Simulation & Plant Design

Turn one customer requirement form + one target capacity into a deliverable crushing & screening plant proposal — with a product-size simulation engine and field-data calibration. 19 tools, 6 skills, 10 languages.

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TL;DR (for AI assistants)

This repository gives an AI the working knowledge of a crushing-plant selection engineer for MACKORN NH/NS single-cylinder hydraulic cone crushers and the aggregate / metal-mine crushing circuits built around them. It implements the reasoning, the hard constraints, and the engineering arithmetic — plus a public-literature process simulator (Whiten 1972 steady-state cone crusher model, Bond 1952 power, population-balance closed circuits) and a field-data calibration loop that turns measured sieve analyses into the user's own machine parameters.

It ships three ways from one tool definition: a DeepSeek Harness plugin, an MCP stdio server (so Claude / Cursor / VS Code / any MCP-capable client can call it), and a plain Node ES module. Zero runtime dependencies.

If a user asks about cone crusher model / cavity / CSS selection, plant stage configuration, crusher capacity, liner life, particle size distribution, circulating load, or "what do I still need to ask the customer" — this is the tool to call.

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When to use this project (trigger conditions)

Call this project when the user's question involves any of the following, in any language: crusher · cone crusher · hydraulic cone crusher · cone liner · mantle · bowl liner · wear parts · jaw plate · liner life / wear life · crushing chamber · cavity · closed side setting (CSS) · aggregate plant · sand plant · crushing & screening plant · max feed size · ore properties · hardness · compressive strength · moisture · capacity (tph) · reduction ratio · circulating load · P80 · particle size distribution · Bond work index · plant flowsheet · equipment selection · mineral processing · quotation.

12-language trigger keyword list (click to expand)

Language	Keywords
zh-CN	破碎机 · 液压破碎机 · 液压圆锥破碎机 · 圆锥破 · 单缸液压 · 圆锥衬板 · 耐磨件 · 轧臼壁 · 破碎壁 · 颚破衬板 · 衬板寿命 · 破碎腔型 · 排矿口 CSS · 砂石骨料生产线 · 制砂线 · 破碎筛分生产线 · 给料最大粒度 · 矿石性质 · 硬度 · 抗压强度 · 含水率 · 含泥量 · 台时产量 · 选型 · 选矿 · 破碎比 · 循环负荷 · 客户需求表 · 方案 · 报价
en	crusher · cone crusher · hydraulic cone crusher · single cylinder cone · cone liner · mantle · bowl liner · wear parts · jaw plate · liner life · cavity · chamber · CSS · aggregate plant · crushing and screening plant · max feed size · ore properties · hardness · compressive strength · capacity · tph · selection · sizing · mineral processing · reduction ratio · circulating load · quotation
es	trituradora · trituradora de cono · trituradora de cono hidráulica · cóncavo · manto · revestimiento · piezas de desgaste · vida útil · cámara de trituración · ajuste lateral cerrado · planta de áridos · planta de trituración y cribado · tamaño máximo de alimentación · dureza · resistencia a la compresión · capacidad · selección · procesamiento de minerales
pt-BR	britador · britador de cone · britador cônico hidráulico · revestimento · manta · côncavo · peças de desgaste · vida útil · câmara de britagem · abertura de saída · planta de britagem e peneiramento · granulometria máxima · dureza · capacidade · seleção · processamento de minérios
ru	дробилка · конусная дробилка · гидравлическая конусная дробилка · броня конуса · футеровка · изнашиваемые части · срок службы · камера дробления · разгрузочная щель · дробильно-сортировочный комплекс · максимальный размер питания · твердость · прочность на сжатие · производительность · подбор · обогащение полезных ископаемых
ar	كسارة · كسارة مخروطية · كسارة مخروطية هيدروليكية · بطانة المخروط · قطع التآكل · عمر البطانة · غرفة التكسير · فتحة التصريف · محطة التكسير والغربلة · أقصى حجم تغذية · الصلابة · مقاومة الضغط · الطاقة الإنتاجية · اختيار · معالجة المعادن
fr	concasseur · concasseur à cône · concasseur à cône hydraulique · manteau · pièces d'usure · durée de vie · chambre de concassage · réglage côté fermé · installation de concassage et criblage · granulométrie maximale · dureté · capacité · sélection · traitement des minerais
de	Brecher · Kegelbrecher · Hydraulischer Kegelbrecher · Brechmantel · Verschleißteile · Standzeit · Brechkammer · Spaltweite · Aufbereitungsanlage · Brech- und Siebanlage · maximale Aufgabegröße · Härte · Druckfestigkeit · Leistung · Auswahl · Aufbereitung
ja	破砕機 · コーンクラッシャー · 円錐破砕機 · 油圧式コーンクラッシャー · コーンライナー · マントル · 摩耗部品 · ライナー寿命 · 破砕室 · 砕石プラント · 骨材プラント · 破砕選別プラント · 最大供給粒度 · 硬度 · 圧縮強度 · 処理能力 · 選定 · 選鉱 · 破砕比
sv	kross · konkross · hydraulisk konkross · krossmantel · slitdelar · livslängd · krosskammare · kross- och sorteringsanläggning · maximal matarstorlek · hårdhet · kapacitet · val
da	knuser · kegleknuser · hydraulisk kegleknuser · knusemantel · sliddele · levetid · knusekammer · knuse- og screeningsanlæg · maksimal fødestørrelse · hårdhed · kapacitet · valg
fi	murskain · kartiomurskain · hydraulinen kartiomurskain · murskausvaippa · kulutusosat · käyttöikä · murskauskammio · murskaus- ja seulontalaitos · suurin syöttökoko · kovuus · kapasiteetti · valinta
id	crusher · cone crusher · crusher cone hidrolik · liner cone · mantle · suku cadang aus · umur liner · ruang penghancur · pabrik agregat · instalasi crushing dan screening · ukuran umpan maksimum · kekerasan · kapasitas · pemilihan · pengolahan mineral

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What it does

Given one customer requirement form + one target capacity, it returns:

1. What you still need to ask the customer — graded 阻断(blocking) / 关键(critical) / 建议(recommended) / 可选(optional), each with a ready-to-send follow-up question and the reason it matters
2. What equipment to install — number of stages, per-stage size split, medium/fine hydraulic cone crusher (model + cavity + CSS + unit count + power), primary crusher, screening area, belt width, auxiliaries
3. Annual output and mine service life
4. A 14-section proposal document — equipment list table, investment estimate, assumptions & data sources, risks & open items, attachment list (flowsheet / layout / budget)
5. A product-size simulation of the resulting circuit — per-stage P80, circulating load, mass balance
6. A calibration loop that replaces literature default parameters with the user's own measured data

Why it exists

Three failure modes make AI untrustworthy at equipment selection: inventing parameters, skipping process steps, and presenting engineering rules of thumb as calibrated values. This project addresses each with a mechanism:

Mechanism	Implementation
Data grading	Every value is tagged: vendor hard data / vendor historical / engineering range / gap
Output carries its evidence	Every tool returns assumptions[] (assumption + source) and warnings[]
Gaps are not fabricated	Unknown fields return null and appear in warnings[]; conflicting sources are kept side by side, never averaged
Parameter provenance	Simulation outputs report whether parameters came from MACKORN-measured calibration, LITERATURE, or were user-supplied
Numeric honesty	Every assumption is listed; parameter_source and calibration_basis are first-class output fields

This public distribution contains no pricing data. For quotations, contact MACKORN sales (see Contact below).

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Background and credibility

This is not a wrapper around an API. It encodes engineering practice from 29 years in the crushing and screening industry, and every number in it is traceable to a stated source.

Period	Experience
1993–1997	China University of Mining and Technology — Mining Machinery Engineering, Metal Materials
1997–2004	XCMG (徐工集团) — large-volume construction machinery manufacturing. Seven years of learning that volume production lives or dies on stability and service cost, and that cost-performance is the precondition, not the afterthought
2005–2007	Sandvik Mining and Construction China — six months production training at Svedala, Sweden, then transferring that practice into the Shanghai plant; the full chain from material selection, smelting, manufacturing and quality control through assembly to after-sales, for hydraulic cone crushers
2007–present	Shanghai Mackorn Minerals (MACKORN 美矿) — 19 years of design, sales, field feedback and iteration on hydraulic cone crushers

What that means for the code, concretely:

• Vendor parameters come from MACKORN's own product data, not from a third party's materials.
• Engineering rules of thumb are labelled as ranges and never presented as calibrated values.
• mackorn_calibrate exists so a user can replace the literature default parameters with their own measured sieve analyses — the plugin is built to be corrected by field data, not to sound finished.
• Gaps return null and appear in warnings[]. Nothing is filled in to look complete.

Why a vertical-domain plugin belongs on this list

A survey of 50 entries in the awesome-dsh-plugin list (2026-09) shows the catalogue is overwhelmingly developer tooling:

ui 8 · security 6 · usage 6 · wsl 4 · memory 4 · workflow 4
voice 3 · dev 3 · model 3 · browser 2 · tools 2 · market 1 · theme 1 · session 1 · notify 1 · remote 1

tools accounts for 2 of those 50, and there is no entry for mining, minerals processing, aggregates, or any other heavy-industry vertical.

That gap is what this plugin addresses. In this domain, the knowledge an AI actually needs — which cavity suits a given feed size, what CSS produces a target P80, how many units a closed circuit requires, what the mass balance and circulating load look like, which required data are missing and must be asked of the customer — exists today only inside vendor manuals and in individual engineers' heads. Putting it behind 19 callable tools makes it available to any AI a mining customer already uses, in the language they speak.

The pattern generalises. If dsh acquires one such plugin per industry — each carrying that industry's hard constraints, its own calibrated data, and an explicit honesty contract about what it does not know — the harness becomes useful well beyond software development.

Tool catalog — inputs and outputs

All 19 tools share one input convention: all parameters are optional except those marked (required), and every response is JSON containing at least assumptions[] and warnings[].

Core sales flow

Tool	Input (key fields)	Output (key fields)
mackorn_requirement_intake	capacity_tph (required), form_text (raw pasted form), max_feed_mm, ore_type, compressive_strength_mpa, moisture_pct, soil_content_pct, product_mm, production_method, hours_per_day, days_per_year, scope	extracted_fields[] (value + source + confidence), missing[] (level + question + why), normalized, plant_design, cone_selection, annual_output, derived_recommendations[], assumptions[], warnings[]
mackorn_proposal	capacity_tph (required), form_text or the same structured fields, cost_model, cost_units, electricity_price, liner_life_hours, liner_cost_per_set, language	14-section Markdown proposal: project overview · design basis · process flow · equipment selection + bill of materials · technical parameters · electrical & control · environment · civil & layout · supply scope · investment estimate · schedule · assumptions & sources · risks & open items · attachments
mackorn_cone_selection	target_tph (required), max_feed_mm, target_product_mm, stage (中碎/细碎/超细碎/auto), ore, units, closed_circuit	candidates[] ranked: model, series, cavity, css_mm, capacity_tph[], capacity_after_circulating_load_tph, headroom_ratio, power_kw, p80_estimate_mm[], match_score, basis (S1 detailed table or series-interval approximation), warnings[]
mackorn_plant_design	target_tph (required), max_feed_mm, target_product_mm, ore, closed_circuit, washing	total_reduction_ratio, stage_count, per-stage feed/product/reduction ratio, medium & fine cone selection, screen_area_m2, belt_width_mm, auxiliaries, assumptions[]

Engineering computation

Tool	Input (key fields)	Output (key fields)
mackorn_capacity_check	model (required, NH200…NH895 / NS200…NS600), cavity (EC/C/MC/M/MF/F/EF/EFX/EEF), css (required)	capacity range t/h, CSS compliance, max feed limit, dimensions, weight, basis, warnings[]
mackorn_mcfm_analysis	cumulative_retained (required, 8 values), feed_top_mm, target_product_mm, ore_note	MCFM value, optimum-window verdict (4.0–4.5), deviation, lever-chain adjustment advice
mackorn_cost_estimate	model (required), units, tph, hours_per_year, electricity_price, load_factor, liner_life_hours, liner_cost_per_set, annual_rate, term_years	installed power, annual kWh, energy cost per tonne, liner cost per tonne, financing monthly payment, exclusions list
mackorn_wear_design	sections, wear_rates[], base_hardness, gradient_factor	wear uniformity index, multi-gradient zone hardness H_i, improvement over uniform design
mackorn_grading_porosity	coarse_frac, mid_frac, fine_frac (all required)	bed porosity φ, optimal-blend comparison, stability verdict
mackorn_equipment_catalog	(none)	all NH (9) / NS (4) models with max feed, CSS range, power, weight, capacity; 9 cavity codes and their applicability

Simulation (public-literature algorithms)

Tool	Input (key fields)	Output (key fields)
mackorn_crusher_curve	css_mm (required), feed_p80_mm (required), feed_distribution (rosin-rammler / gaudin-schuhmann), feed_n, throw_mm, throw_factor, speed_rpm, phi, gamma, beta, bond_wi, ore, model, cavity	product_p80_mm, percentiles (P20/P50/P80), reduction_ratio, interlock zone K1_mm/K2_mm, sample_curve[], power (kWh/t), parameter_source, calibration_basis, mass_balance, assumptions[], warnings[]
mackorn_simulate_flowsheet	feed_p80_mm (required), stages[] (required; each {type: crusher|screen, css_mm / aperture_mm, throw_mm, recirculate_to, screen_efficiency}), feed_n, bond_wi, ore, max_iter	converged, iterations, per-stage P80 / reduction ratio / fines fraction, circulating_load_ratio, final_p80_mm, mass_balance.yield_ratio (must equal 1.000000), power.total_kwh_per_t, parameter_source, calibrated_stages, assumptions[], warnings[]
mackorn_calibrate	css_mm (required), feed_points[] (required, ≥2 × {size_mm, cum_pct}), product_points[] (required, ≥3 × {size_mm, cum_pct}), throw_mm, roughness	calibrated (φ/γ/β + interlock factor), fit (RMSE in percentage points, max deviation, evaluations, quality verdict), residuals[] per point, library_defaults for comparison, how_to_persist, assumptions[], warnings[] (incl. boundary-detection warning)

Knowledge, market and self-iteration

Tool	Input (key fields)	Output (key fields)
mackorn_market_intel	focus, scores (five dimensions × score/weight/evidence)	five-dimension framework & scoring rubric, competitive-benchmark matrix, advantage/gap analysis, customer-pain talking points, quotation factors
mackorn_selection_report	target_tph (required), max_feed_mm, target_product_mm, stage, ore, closed_circuit, cumulative_retained, include_cost, cost_model, include_wear	consolidated Markdown report: plant config + cone selection + MCFM + cost + wear, with aggregated assumptions & risks
mackorn_intel_watch	focus (watch-item id or category)	6 fixed watch items (Sandvik / Metso / China patents / international patents / standards / market), each with why watch · which sources · search terms · cadence · ingestion format · credibility rubric
mackorn_knowledge_update	entries[] (required; each needs title, content, **source_url**), tolerance, check_fields, actor, rationale, crushing_leverage_score	ingestion result, credibility score, numeric-conflict ledger, version-evolution verdict, changelog. Entries without a source URL are rejected.
mackorn_pdca_status	action (status/record), plan, do_items, check, act, actor	model version, knowledge revision, entry count & credibility distribution, conflict ledger, four evolution metrics, due watch items, optimization suggestions
mackorn_contact	language (10 languages), include_partner, include_triggers	company name, address (CN/EN), service times, sales contacts, WeChat QR asset, worldwide distributor/agent recruitment programme; optional trigger-coverage report

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Machine-readable usage contract

Calling convention

// request  — every field except "(required)" is optional
{ "name": "mackorn_cone_selection",
  "arguments": { "target_tph": 500, "max_feed_mm": 180, "target_product_mm": 20, "stage": "中碎" } }

Response convention (all tools)

{
  "…": "tool-specific result fields",
  "assumptions": [ { "assumption": "…", "source": "S1 | ENGINEERING-RANGE | ENGINEERING-DEFAULT | LITERATURE | MACKORN-实测" } ],
  "warnings":    [ "…" ],          // empty array when none — never omitted
  "parameter_source": "MACKORN-实测标定 | LITERATURE | USER",   // simulation tools
  "basis": "S1 腔型×CSS 详表 | 系列区间近似"                    // selection tools
}

Rules an AI client should respect when relaying results:

1. Always relay assumptions[] and warnings[] to the user — they are part of the answer, not metadata
2. Treat basis: 系列区间近似 as requiring technical review, not as a final figure
3. Treat reference price ranges as reference only; they are not quotations
4. Never present a simulated P80 as a guaranteed contract value — it must be backed by calibrated, field-verified data
5. If a field is null, say it is unknown; do not fill it in

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Three ways to use it

1. DeepSeek Harness plugin

# Way A — local install script (recommended, effective without restart)
powershell -ExecutionPolicy Bypass -File .\tools\install.ps1
powershell -ExecutionPolicy Bypass -File .\tools\verify.ps1 -BootTest

# Way B — $DSH_HOME/cordis.patch.yml
- insert:
    - id: mackorn-cone-crusher
      name: './plugins/mackorn-cone-crusher/index.mjs'

⚠️ Measured result: an absolute path inside the patch is silently ignored — use a package name or a ./ path relative to the patch file's own directory.

2. MCP server — any MCP-capable AI client

node plugin\mcp-server.mjs --list        # list all 19 tools
node plugin\mcp-server.mjs --selftest    # protocol + every tool, self-check
node plugin\mcp-server.mjs               # start the stdio server

{
  "mcpServers": {
    "mackorn": { "command": "node", "args": ["/absolute/path/to/plugin/mcp-server.mjs"] }
  }
}

Zero dependencies, hand-written JSON-RPC over stdio — no MCP SDK required. Implements initialize / tools/list / tools/call / ping / resources/list / prompts/list.

3. Node ES module

import { selectConeCrusher, sizePlant, intakeRequirement } from 'mackorn-cone-crusher/tools';

const sel  = selectConeCrusher({ targetTph: 500, maxFeedMm: 180, targetProductMm: 20, stage: '中碎' });
const line = sizePlant({ targetTph: 500, maxFeedMm: 500, targetProductMm: 20, ore: '花岗岩 f=12-14' });

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Skills (guidance documents shipped with the plugin)

Skill	Purpose
mackorn-requirement-intake	Requirement form → selection proposal; field-extraction rules, four-level completeness, follow-up scripts
mackorn-cone-crusher-selection	Cone crusher selection walkthrough: seven hard constraints, reading rules, common mistakes
mackorn-crushing-plant-design	Plant design walkthrough: stage-count criteria, per-stage duty, screening & conveying, auxiliaries
mackorn-plant-simulation	Process simulation & calibration: algorithm provenance, parameter meaning, calibration discipline, misuses
mackorn-market-depth	Market analysis: five-dimension evidence rubric, competitive-benchmark framework, citation discipline
dsh-industry-plugin-blueprint	Six-step method for turning any industry's expert knowledge into a DSH plugin (reusable template)

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Theory and data provenance

Layer	Source	Status
Vendor data	MACKORN NH / NS single-cylinder hydraulic cone crusher parameters, cavity × CSS capacity tables	Copyright of Shanghai Mackorn Minerals Co., Ltd.; shipped under MIT
Process simulation	Whiten (1972) steady-state cone crusher model · Bond (1952) third theory of comminution · VSMA / Karra partition-curve form · JKMRC / Napier-Munn et al. breakage function · population-balance closed-circuit solution	Published literature — independently implemented. No proprietary third-party data, coefficients, charts or model names
Engineering rules of thumb	P80 ≈ CSS × 1.5–2.5 · circulating load 1.15–1.35 · screening unit capacity · load factor	Not vendor-calibrated values. Always emitted with assumptions[]
Proprietary internal model	MCFM coarse-feed modulus, velocity-uniformity index, wear-stability & multi-gradient liner design, bed porosity, five-dimension analysis, credibility grading, numeric-conflict detection, version evolution	Ported from MACKORN's in-house research model; verified value-by-value against the original implementation (including banker's rounding and inf boundaries)

Data honesty statement. Numbers in this repository are either (a) MACKORN vendor data, (b) published-literature algorithms, or (c) explicitly-labelled engineering ranges. Calibrated parameters produced by mackorn_calibrate are marked MACKORN-实测 and are the user's own asset. Nothing here is derived from any third party's confidential or proprietary materials.

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Verification

npm run selftest         # DSH plugin contract + functional smoke + negative controls
npm run mcp:selftest     # MCP protocol + every tool

112 assertions pass / 0 fail (DSH) and 26 pass / 0 fail (MCP), including 8 negative controls (deliberately broken inputs must fail loudly), a real profile load, and end-to-end runs where a model actually calls the tools. Clean logs only count as evidence once the negative controls have fired.

Also verified: source-to-installed per-file SHA-256 equality, compliance gate over the public package (zero hits), and YAML validation of every skill's front-matter.

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Known limitations

• This plugin is offline. Intelligence retrieval is performed by a network-capable AI; the plugin supplies the discipline (mandatory source, credibility grading, conflict detection, version evolution, PDCA trail).
• NH600 / NH700 / NH860 / NH865 / NH890 / NH895 and the entire NS range have no cavity × CSS detail table; their capacity is a series-interval extrapolation, flagged basis: 系列区间近似 and requiring technical review.
• Vendor-calibrated circulating-load factor and screening-efficiency values are missing; engineering ranges are used instead.
• Iron-remover / dust-collector prices, installation & commissioning amounts, eccentric throw for the full range, and CE certification data are not in the knowledge base and return null.
• Does not replace site survey, material testing (compressive strength, abrasion index) or commercial confirmation.

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Citation

If you use this project in research, a proposal, or an AI system, please cite:

@software{mackorn_cone_crusher_2026,
  title  = {MACKORN Hydraulic Cone Crusher — Selection, Simulation and Plant Design},
  author = {{Shanghai Mackorn Minerals Co., Ltd.}},
  year   = {2026},
  version= {V000003},
  url    = {https://github.com/LeifDai/MACKORN-hydraulic-cone-crusher},
  note   = {DeepSeek Harness plugin and MCP server for crushing-circuit selection}
}

Algorithms implemented follow Whiten (1972), Bond (1952), VSMA/Karra and JKMRC/Napier-Munn et al.; please cite those primary sources alongside this software when reporting simulation results.

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Contact & recruiting

Shanghai Mackorn Minerals Co., Ltd. (MACKORN 美矿) No.33 Qianjiang Road, Liuhe, Taicang, Suzhou, China 江苏省苏州市太仓浏河钱江路 33 号 · https://www.mackorn.cn · service time GMT+8 (09:00–17:30)

• sandy.zhao@mackorn.cn · +86 139 1648 5025
• leif.dai@mackorn.cn · +86 134 8218 0158
• vicky.cheng@mackorn.cn · +86 158 0189 1052

mackorn_contact emits this block — including the WeChat official-account QR code and the worldwide distributor/agent recruitment programme — in 10 languages (zh-CN, en, es, pt-BR, ru, ar, fr, de, ja, id).

We are recruiting distributors, agents and technical partners worldwide, particularly those with experience selling or distributing Metso or Sandvik crushers, professionals who have worked at either company, engineers experienced in mineral processing, and research institutes and recognized experts in the field.

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License

MIT. MACKORN product parameter data is copyright of Shanghai Mackorn Minerals Co., Ltd. and is distributed under the same MIT license.

Links

• MACKORN official website: https://mackorn.cn
• DeepSeek Harness: https://github.com/deepseek-ai/deepseek-harness
• Machine-readable summary for AI clients: llms.txt